This is an automated email from the ASF dual-hosted git repository.
aradzinski pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-nlpcraft-website.git
The following commit(s) were added to refs/heads/master by this push:
new 419d8b3 WIP.
419d8b3 is described below
commit 419d8b3c3a11ceab6c459ed55b82a85bdbf2fc05
Author: Aaron Radzinski <aradizn...@apache.org>
AuthorDate: Tue Jul 27 20:26:39 2021 -0700
WIP.
---
blogs/nlpcraft-idl-intent-definition-language.html | 9 +-
blogs/short_term_memory.html | 8 +-
intent-matching.html | 153 +++++---
short-term-memory.html | 413 ++++++++++++++++++++-
4 files changed, 503 insertions(+), 80 deletions(-)
diff --git a/blogs/nlpcraft-idl-intent-definition-language.html
b/blogs/nlpcraft-idl-intent-definition-language.html
index d2c6994..d6f625e 100644
--- a/blogs/nlpcraft-idl-intent-definition-language.html
+++ b/blogs/nlpcraft-idl-intent-definition-language.html
@@ -113,7 +113,6 @@ publish_date: June 3, 2021
<pre class="brush: idl">
intent=xb
flow=/#flowModelMethod/
- ordered=true
term(a)=/org.mypackage.MyClass#termMethod/?
fragment(frag)
</pre>
@@ -174,7 +173,7 @@ publish_date: June 3, 2021
NLPCraft IDL has only 10 keywords: <code>flow, fragment, import,
intent, meta, ordered, term, true, false, null.</code>
</p>
<ul class="fixed">
- <li><code>intent, flow, fragment, meta, ordered, term</code> are parts
of the intent definition.</li>
+ <li><code>intent, flow, fragment, meta, options, term</code> are parts
of the intent definition.</li>
<li><code>fragment</code> keyword is also can be used to create named
terms lists to include in intent definitions (a-la macros).</li>
<li><code>import</code> - required for including external files with
fragment, intent or imports statements.</li>
<li><code>true, false, null</code> - used when working with built-in
functions.</li>
@@ -418,12 +417,6 @@ publish_date: June 3, 2021
Optional element. A additional dataset that can be used by
term predicates presented in JSON format.
</p>
</li>
- <li>
- <p><b>Ordered Flag</b></p>
- <p>
- Optional element. The default is <code>false</code>. Defines
whether or not the order of the terns in the intent is important.
- </p>
- </li>
</ul>
</section>
<section>
diff --git a/blogs/short_term_memory.html b/blogs/short_term_memory.html
index b661bd7..3ef977c 100644
--- a/blogs/short_term_memory.html
+++ b/blogs/short_term_memory.html
@@ -107,10 +107,10 @@ publish_date: July 26, 2019
</ul>
<div class="bq info">
<div style="display: inline-block; margin-bottom: 20px">
- <a style="margin-right: 10px" target="opennlp"
href="https://opennlp.apache.org";><img src="/images/opennlp-logo.png"
height="32px" alt=""></a>
- <a style="margin-right: 10px" target="google"
href="https://cloud.google.com/natural-language/";><img
src="/images/google-cloud-logo-small.png" height="32px" alt=""></a>
- <a style="margin-right: 10px" target="stanford"
href="https://stanfordnlp.github.io/CoreNLP";><img
src="/images/corenlp-logo.gif" height="48px" alt=""></a>
- <a style="margin-right: 10px" target="spacy"
href="https://spacy.io";><img src="/images/spacy-logo.png" height="32px"
alt=""></a>
+ <a style="margin-right: 10px" target=_
href="https://opennlp.apache.org";><img src="/images/opennlp-logo-h32.png"
alt=""></a>
+ <a style="margin-right: 10px" target=_
href="https://cloud.google.com/natural-language/";><img
src="/images/google-cloud-logo-small-h32.png" alt=""></a>
+ <a style="margin-right: 10px" target=_
href="https://stanfordnlp.github.io/CoreNLP";><img
src="/images/corenlp-logo-h48.png" alt=""></a>
+ <a style="margin-right: 10px" target=_
href="https://spacy.io";><img src="/images/spacy-logo-h32.png" alt=""></a>
</div>
<p>
Note that NLPCraft provides <a
href="/integrations.html">support</a> for wide variety of named entities (with
all built-in ones being properly normalized)
diff --git a/intent-matching.html b/intent-matching.html
index 85f9fcb..4dc8f3f 100644
--- a/intent-matching.html
+++ b/intent-matching.html
@@ -32,20 +32,20 @@ id: intent_matching
<section id="intent">
<h2 class="section-title">Intent <a href="#"><i class="top-link fas
fa-fw fa-angle-double-up"></i></a></h2>
<p>
- The goal of the data model implementation is to take the input
utterance and
+ The goal of the data model implementation is to take the user
input text and
match it to a specific user-defined code that will execute for
that input. The mechanism that
provides this matching is called an <em>intent</em>.
</p>
<p>
- The intent refers to the goal that the end-user had in mind when
speaking or typing the input utterance.
- The intent has a <em>declarative part or template</em> written in
<a href="#idl">Intent Definition Language</a> that describes
- a particular form or type of the input utterance.
+ The intent generally refers to the goal that the end-user had in
mind when speaking or typing the input utterance.
+ The intent has a <em>declarative part or template</em> written in
<a href="#idl">Intent Definition Language</a> that strictly defines
+ a particular form the user input.
Intent is also <a href="#binding">bound</a> to a callback method
that will be executed when that intent, i.e. its template, is detected as the
best match
- for a given input utterance. A typical data model will have
multiple intents defined for each form of the expected user input
+ for a given input input. A typical data model will have multiple
intents defined for each form of the expected user input
that model wants to react to.
</p>
<p>
- For example, a data model for banking chat bot or analytics
application can have multiple intents for each domain-specific group of
utterances such as
+ For example, a data model for banking chat bot or analytics
application can have multiple intents for each domain-specific group of input
such as
opening an account, closing an account, transferring money,
getting statements, etc.
</p>
<p>
@@ -58,7 +58,7 @@ id: intent_matching
<h2 class="section-title">IDL - Intent Definition Language <a
href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
<p>
NLPCraft intents are written in Intent Definition Language (IDL).
- IDL is a relatively straightforward and simple language. For
example,
+ IDL is a relatively straightforward declarative language. For
example,
here's a simple intent <code>x</code> with two terms
<code>a</code> and <code>b</code>:
</p>
<pre class="brush: idl">
@@ -67,40 +67,12 @@ id: intent_matching
term(b)={has(tok_groups(), "my_group")}
</pre>
<p>
- IDL intent defines a match between the parsed input utterance,
i.e. the collection of
+ IDL intent defines a match between the parsed user input
represented as the collection of
<a class="not-code" target="javadoc"
href="/apis/latest/org/apache/nlpcraft/model/NCToken.html">tokens</a>,
- and the user-define callback method. To accomplish that, IDL
through its <a href="#idl_functions">functions</a> provides
- access to the following information:
+ and the user-define callback method. IDL intents are <a
href="#binding">bound</a> to their callbacks via <a href="#binding">Java
+ annotation</a> and can be <a href="#idl_location">located</a> in
the same Java annotations or placed in model YAML/JSON file
+ as well as in external <code>*.idl</code> files.
</p>
- <ul>
- <li>
- Basic <a class="not-code" target="javadoc"
href="/apis/latest/org/apache/nlpcraft/model/NCToken.html">token properties</a>
such as token ID,
- group membership, aliases, token parent and ancestry hierarchy,
- token's constituent part tokens, abstract flag, etc.
- </li>
- <li>
- NLP <a class="not-code" target="javadoc"
href="/apis/latest/org/apache/nlpcraft/model/NCToken.html">token properties</a>
- stem,
- lemma, PoS tag, and variety of computed boolean flags
- such as stop word, swear word, free word, known dictionary
word,
- bracketed, quoted, etc.
- </li>
- <li>
- <a class="not-code" target="javadoc"
href="/apis/latest/org/apache/nlpcraft/model/NCToken.html">Information</a> about
- how particular token was found in the input, i.e. its sparsity
value, permutation
- flag, synonym value, indices in the original text, etc.
- </li>
- <li>
- Additional token metadata provided by NER providers. For
example, for geographical token additional metadata
- can provide population, postal codes, latitude and longitude
coordinates, and other demographic information.
- </li>
- <li>
- Additional metadata about user and company that made the input
request like admin status, signup date,
- first and last names, avatars, emails, company name, website,
address, etc.
- </li>
- <li>
- System information such as current date and time, OS
properties, system and environment variables.
- </li>
- </ul>
<p>
You can review the formal
<a target="github"
href="https://github.com/apache/incubator-nlpcraft/blob/master/nlpcraft/src/main/scala/org/apache/nlpcraft/model/intent/compiler/antlr4/NCIdl.g4";>ANTLR4
grammar</a> for IDL,
@@ -133,7 +105,7 @@ id: intent_matching
Numeric literals use Java string conversions.
</li>
<li>
- IDL has only 10 reserved keywords: <code>flow fragment import
intent meta ordered term true false null</code>
+ IDL has only 10 reserved keywords: <code>flow fragment import
intent meta options term true false null</code>
</li>
<li>
Identifiers and literals can use the same Unicode space as
Java.
@@ -180,8 +152,14 @@ id: intent_matching
}
intent=xb
+ options={
+ 'ordered': false,
+ 'unused_free_words': true,
+ 'unused_sys_toks': true,
+ 'unused_usr_toks': false,
+ 'allow_stm_only': false
+ }
flow=/#flowModelMethod/
- ordered=true
term(a)=/org.mypackage.MyClass#termMethod/?
fragment(frag, {'p1': 25, 'p2': {'a': false}})
</pre>
@@ -195,19 +173,78 @@ id: intent_matching
<code>xa</code> and <code>xb</code> are the mandatory
intent IDs. Intent ID is any arbitrary unique string matching the following
lexer template:
<code>(UNI_CHAR|UNDERSCORE|LETTER|DOLLAR)+(UNI_CHAR|DOLLAR|LETTER|[0-9]|COLON|MINUS|UNDERSCORE)*</code>
</dd>
- <dt><code>ordered=true</code> <sup><small>line
14</small></sup></dt>
+ <dt><code>options={...}</code> <sup><small>line
13</small></sup></dt>
<dd>
<em>Optional.</em>
- Whether or not this intent is ordered. Default is
<code>false</code>.
- For ordered intent the specified order of terms is
important for matching this intent.
- If intent is unordered its terms can be found in any
order in the input text.
- Note that ordered intent significantly limits the user
input it can match. In most cases
- the ordered intent is only applicable to processing of
a formal grammar (like a programming language)
- and mostly unsuitable for the natural language
processing.
+ Matching options specified as JSON object. Entire JSON
object as well as each individual JSON field is optional. Allows
+ to customize the matching algorithm for this intent:
+ <table class="gradient-table">
+ <thead>
+ <tr>
+ <td>Option</td>
+ <td>Description</td>
+ <td>Default Value</td>
+ </tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td><code>ordered</code></td>
+ <td>
+ Whether or not this intent is ordered.
+ For ordered intent the specified order
of terms is important for matching this intent.
+ If intent is unordered its terms can
be found in any order in the input text.
+ Note that ordered intent significantly
limits the user input it can match. In most cases
+ the ordered intent is only applicable
to processing of a formal grammar (like a programming language)
+ and mostly unsuitable for the natural
language processing.
+ </td>
+ <td><code>false</code></td>
+ </tr>
+ <tr>
+ <td><code>unused_free_words</code></td>
+ <td>
+ Whether or not free words - that are
unused by intent matching - should be
+ ignored (value <code>true</code>) or
reject the intent match (value <code>false</code>).
+ Free words are the words in the user
input that were not recognized as any user or system
+ token. Typically, for the natural
language comprehension it is safe to ignore free
+ words. For the formal grammar,
however, this could make the matching logic too loose.
+ </td>
+ <td><code>true</code></td>
+ </tr>
+ <tr>
+ <td><code>unused_sys_toks</code></td>
+ <td>
+ Whether or not unused <a
href="/data-model.html#builtin">system tokens</a> should be
+ ignored (value <code>true</code>) or
reject the intent match (value <code>false</code>).
+ By default, tne unused system tokens
are ignored.
+ </td>
+ <td><code>true</code></td>
+ </tr>
+ <tr>
+ <td><code>unused_usr_toks</code></td>
+ <td>
+ Whether or not unused user-defined
tokens should be
+ ignored (value <code>true</code>) or
reject the intent match (value <code>false</code>).
+ By default, tne unused user tokens are
not ignored since it is assumed that user would
+ defined his or her own tokens on
purpose and construct the intent logic appropriate.
+ </td>
+ <td><code>false</code></td>
+ </tr>
+ <tr>
+ <td><code>allow_stm_only</code></td>
+ <td>
+ Whether or not the intent can match
when all of the matching tokens came from STM.
+ By default, this special case is
disabled (value <code>false</code>). However, in specific intents
+ designed for free-form language
comprehension scenario, like, for example, SMS messaging - you
+ may want to enable this option.
+ </td>
+ <td><code>false</code></td>
+ </tr>
+ </tbody>
+ </table>
</dd>
<dt>
<code>flow="^(?:login)(^:logout)*$"</code>
<sup><small>line 2</small></sup><br/>
- <code>flow=/#flowModelMethod/</code> <sup><small>line
13</small></sup>
+ <code>flow=/#flowModelMethod/</code> <sup><small>line
20</small></sup>
</dt>
<dd>
<p>
@@ -276,7 +313,7 @@ id: intent_matching
<code style="padding-left: 20px">@usrTypes =
meta_model('user_types')</code><br>
<code style="padding-left: 20px">(tokId == 'order' ||
tokId == 'order_cancel') && has_all(@usrTypes, list(1, 2, 3))</code><br>
<code>}</code><br>
-
<code>term(a)=/org.mypackage.MyClass#termMethod/?</code> <sup><small>line
15</small></sup>
+
<code>term(a)=/org.mypackage.MyClass#termMethod/?</code> <sup><small>line
21</small></sup>
</dt>
<dd>
<p>
@@ -392,7 +429,7 @@ id: intent_matching
</p>
</dd>
<dt>
- <code>fragment(frag, {'p1': 25, 'p2': {'a':
false}})</code> <sup><small>line 16 </small></sup><br>
+ <code>fragment(frag, {'p1': 25, 'p2': {'a':
false}})</code> <sup><small>line 22 </small></sup><br>
</dt>
<dd>
<p>
@@ -507,7 +544,7 @@ id: intent_matching
</p>
<h2 class="section-sub-title">Intent Examples <a href="#"><i
class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
<p>
- Here's number of intent examples with explanations:
+ Here's few of intent examples with explanations:
</p>
<p>
<b>Example 1:</b>
@@ -526,10 +563,13 @@ id: intent_matching
Intent uses default conversational support (<code>true</code>)
and default order (<code>false</code>).
</li>
<li>
- Intent has two conversational terms that have to be found for
the intent to match. Note that second
+ Intent has two conversational terms (<code>~</code>) that have
to be found for the intent to match. Note that second
term is optional as it has <code>[0,2]</code> quantifier.
</li>
<li>
+ Both terms have to be found in the user input for the intent
to match.
+ </li>
+ <li>
First term matches any single token with ID <code>x:id</code>.
</li>
<li>
@@ -564,7 +604,10 @@ id: intent_matching
<code>id2</code> somewhere in the history of previously
matched intents in the course of the current conversation.
</li>
<li>
- Intent has two non-conversational terms. Both terms have to be
present only once (their implicit quantifiers are <code>[1,1]</code>).
+ Intent has two non-conversational terms (<code>=</code>). Both
terms have to be present only once (their implicit quantifiers are
<code>[1,1]</code>).
+ </li>
+ <li>
+ Both terms have to be found in the user input for the intent
to match.
</li>
<li>
First term should be a token with ID <code>mytok</code> and
have metadata property <code>score</code> of type
@@ -632,7 +675,7 @@ id: intent_matching
<tr><td><code>java.lang.Boolean</code></td><td><code>Boolean</code></td><td>You
can use <code><b>true</b></code> or <code><b>false</b></code>
literals.</td></tr>
<tr><td><code>java.util.List<T></code></td><td><code>List[T]</code></td><td>Use
<code>list(...)</code> IDL function to create new list.</td></tr>
<tr><td><code>java.util.Map<K,V></code></td><td><code>Map[K,V]</code></td><td></td></tr>
- <tr><td><code><a target="javadoc"
href="/apis/latest/org/apache/nlpcraft/model/NCToken">NCToken</a></code></td><td><code>Token</code></td><td></td></tr>
+ <tr><td><code><a target="javadoc"
href="/apis/latest/org/apache/nlpcraft/model/NCToken.html">NCToken</a></code></td><td><code>Token</code></td><td></td></tr>
<tr><td><code>java.lang.Object</code></td><td><code>Any</code></td><td>Any of
the supported types above. Use <code><b>null</b></code> literal for null
value.</td></tr>
</tbody>
</table>
diff --git a/short-term-memory.html b/short-term-memory.html
index 9d182a6..255294b 100644
--- a/short-term-memory.html
+++ b/short-term-memory.html
@@ -24,20 +24,12 @@ id: short_term_memory
<div id="short-term-memory" class="col-md-8 second-column">
<section id="stm">
<h2 class="section-title">Conversation <span class="amp">&</span> STM
<a href="#"><i class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
- <div class="bq info">
- <b>Short-Term Memory</b>
- <p>
- Read more in-depth explanation about maintaining
conversational context and
- Short-Term Memory in this <a
href="/blogs/short_term_memory.html">blog.</a>
- </p>
- </div>
-
<p>
- NLPCraft provides automatic conversation management right out of
the box.
- Conversation management is based on the idea of short-term memory
(STM). STM is automatically
- maintained by NLPCraft per each user and data model. Essentially,
NLPCraft "remembers"
- the context of the conversation and can supply the currently
missing elements from its memory (i.e. from STM).
- STM implementation is also fully integrated with intent matching.
+ NLPCraft provides automatic conversation management right out of
the box that is fully integrated with
+ <a href="/intent-matching.html">intent matching</a>. Conversation
management is based on the idea of short-term memory (STM). STM is automatically
+ maintained by NLPCraft per each user and data model combination.
Essentially, NLPCraft "remembers"
+ the context of the conversation and can supply the currently
missing elements from STM
+ to the intent matching algorithm for the conversational terms.
</p>
<p>
Maintaining conversation state is necessary for effective context
resolution, so that users
@@ -120,6 +112,401 @@ id: short_term_memory
via <a
href="https://github.com/apache/incubator-nlpcraft/blob/master/openapi/nlpcraft_swagger.yml";
target="github">REST API</a>.
</p>
</section>
+ <section>
+ {% include latest_ver_blog_warn.html %}
+ <p>
+ In this blog, I'll try to give a high-level overview of STM -
Short-Term Memory, a technique used to
+ maintain conversational context in NLPCraft. Maintaining the
proper conversation context - remembering
+ what the current conversation is about - is essential for all
human interaction and thus essential for
+ computer-based natural language understanding. To my knowledge,
NLPCraft provides one of the most advanced
+ implementations of STM, especially considering how tightly it is
integrated with NLPCraft's unique
+ intent-based matching (Google's <a target=google
href="https://cloud.google.com/dialogflow/";>DialogFlow</a> is very similar yet).
+ </p>
+ <p>
+ Let's dive in.
+ </p>
+ </section>
+ <section>
+ <h2 class="section-title">Parsing User Input <a href="#"><i
class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
+ <p>
+ One of the key objectives when parsing user input sentence for
Natural Language Understanding (NLU) is to
+ detect all possible semantic entities, a.k.a <em>named
entities</em>. Let's consider a few examples:
+ </p>
+ <ul>
+ <li>
+ <code>"What's the current weather in Tokyo?"</code><br/>
+ This sentence is fully sufficient for the processing
+ since it contains the topic <code>weather</code> as well as
all necessary parameters
+ like time (<code>current</code>) and location
(<code>Tokyo</code>).
+ </li>
+ <li>
+ <code>"What about Tokyo?"</code><br/>
+ This is an unclear sentence since it does not have the subject
of the
+ question - what is it about Tokyo?
+ </li>
+ <li>
+ <code>"What's the weather?"</code><br/>
+ This is also unclear since we are missing important parameters
+ of location and time for our request.
+ </li>
+ </ul>
+ <p>
+ Sometimes we can use default values like the current user's
location and the current time (if they are missing).
+ However, this can lead to the wrong interpretation if the
conversation has an existing context.
+ </p>
+ <p>
+ In real life, as well as in NLP-based systems, we always try to
start a conversation with a fully defined
+ sentence since without a context the missing information cannot be
obtained and the sentenced cannot be interpreted.
+ </p>
+ </section>
+ <section>
+ <h2 class="section-title">Semantic Entities <a href="#"><i
class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
+ <p>
+ Let's take a closer look at the named entities from the above
examples:
+ </p>
+ <ul>
+ <li>
+ <code>weather</code> - this is an indicator of the subject of
the conversation. Note that it indicates
+ the type of question rather than being an entity with multiple
possible values.
+ </li>
+ <li>
+ <code>current</code> - this is an entity of type
<code>Date</code> with the value of <code>now</code>.
+ </li>
+ <li>
+ <code>Tokyo</code> - this is an entity of type
<code>Location</code> with two values:
+ <ul>
+ <li><code>city</code> - type of the location.</li>
+ <li><code>Tokyo, Japan</code> - normalized name of the
location.</li>
+ </ul>
+ </li>
+ </ul>
+ <p>
+ We have two distinct classes of entities:
+ </p>
+ <ul>
+ <li>
+ Entities that have no values and only act as indicators or
types. The entity <code>weather</code> is the
+ type indicator for the subject of the user input.
+ </li>
+ <li>
+ Entities that additionally have one or more specific values
like <code>current</code> and <code>Tokyo</code> entities.
+ </li>
+ </ul>
+ <div class="bq info">
+ <div style="display: inline-block; margin-bottom: 20px">
+ <a style="margin-right: 10px" target=_
href="https://opennlp.apache.org";><img src="/images/opennlp-logo-h32.png"
alt=""></a>
+ <a style="margin-right: 10px" target=_
href="https://cloud.google.com/natural-language/";><img
src="/images/google-cloud-logo-small-h32.png" alt=""></a>
+ <a style="margin-right: 10px" target=_
href="https://stanfordnlp.github.io/CoreNLP";><img
src="/images/corenlp-logo-h48.png" alt=""></a>
+ <a style="margin-right: 10px" target=_
href="https://spacy.io";><img src="/images/spacy-logo-h32.png" alt=""></a>
+ </div>
+ <p>
+ Note that NLPCraft provides <a
href="/integrations.html">support</a> for wide variety of named entities (with
all built-in ones being properly normalized)
+ including <a href="/integrations.html">integrations</a> with
+ <a target="spacy" href="https://spacy.io/";>spaCy</a>,
+ <a target="stanford"
href="https://stanfordnlp.github.io/CoreNLP";>Stanford CoreNLP</a>,
+ <a target="opennlp"
href="https://opennlp.apache.org/";>OpenNLP</a> and
+ <a target="google"
href="https://cloud.google.com/natural-language/";>Google Natural Language</a>.
+ </p>
+ </div>
+ </section>
+ <section>
+ <h2 class="section-title">Incomplete Sentences <a href="#"><i
class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
+ <p>
+ Assuming previously asked questions about the weather in Tokyo (in
the span of the ongoing conversation) one
+ could presumably ask the following questions using a <em>shorter,
incomplete</em>, form:
+ </p>
+ <ul>
+ <li>
+ <code>"What about Kyoto?</code><br/>
+ This question is missing both the subject and the time.
However, we
+ can safely assume we are still talking about current weather.
+ </li>
+ <li>
+ <code>"What about tomorrow?"</code><br/>
+ Like above we automatically assume the weather subject but
+ use <code>Kyoto</code> as the location since it was mentioned
the last.
+ </li>
+ </ul>
+ <p>
+ These are incomplete sentences. This type of short-hands cannot be
interpreted without prior context (neither
+ by humans or by machines) since by themselves they are missing
necessary information.
+ In the context of the conversation, however, these incomplete
sentences work. We can simply provide one or two
+ entities and rely on the <em>"listener"</em> to recall the rest of
missing information from a
+ <em>short-term memory</em>, a.k.a conversation context.
+ </p>
+ <p>
+ In NLPCraft, the intent-matching logic will automatically try to
find missing information in the
+ conversation context (that is automatically maintained). Moreover,
it will properly treat such recalled
+ information during weighted intent matching since it naturally has
less "weight" than something that was
+ found explicitly in the user input.
+ </p>
+ </section>
+ <section>
+ <h2 class="section-title">Short-Term Memory <a href="#"><i
class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
+ <p>
+ The short-term memory is exactly that... a memory that keeps only
small amount of recently used information
+ and that evicts its contents after a short period of inactivity.
+ </p>
+ <p>
+ Let's look at the example from a real life. If you would call your
friend in a couple of hours asking <code>"What about a day after?"</code>
+ (still talking about weather in Kyoto) - he'll likely be
thoroughly confused. The conversation is timed out, and
+ your friend has lost (forgotten) its context. You will have to
explain again to your confused friend what is that you are asking about...
+ </p>
+ <p>
+ NLPCraft has a simple rule that 5 minutes pause in conversation
leads to the conversation context reset. However,
+ what happens if we switch the topic before this timeout elapsed?
+ </p>
+ </section>
+ <section>
+ <h2 class="section-title">Context Switch <a href="#"><i
class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
+ <p>
+ Resetting the context by the timeout is, obviously, not a hard
thing to do. What can be trickier is to detect
+ when conversation topic is switched and the previous context needs
to be forgotten to avoid very
+ confusing interpretation errors. It is uncanny how humans can
detect such switch with seemingly no effort, and yet
+ automating this task by the computer is anything but effortless...
+ </p>
+ <p>
+ Let's continue our weather-related conversation. All of a sudden,
we ask about something completely different:
+ </p>
+ <ul>
+ <li>
+ <code>"How much mocha latter at Starbucks?"</code><br/>
+ At this point we should forget all about previous conversation
about weather and assume going forward
+ that we are talking about coffee in Starbucks.
+ </li>
+ <li>
+ <code>"What about Peet's?"</code><br/>
+ We are talking about latter at Peet's.
+ </li>
+ <li>
+ <code>"...and croissant?"</code><br/>
+ Asking about Peet's crescent-shaped fresh rolls.
+ </li>
+ </ul>
+ <p>
+ Despite somewhat obvious logic the implementation of context
switch is not an exact science. Sometimes, you
+ can have a "soft" context switch where you don't change the topic
of the conversation 100% but yet sufficiently
+ enough to forget at least some parts of the previously collected
context. NLPCraft has a built-in algorithm
+ to detect the hard switch in the conversation. It also exposes API
to perform a selective reset on the
+ conversation in case of "soft" switch.
+ </p>
+ </section>
+ <section>
+ <h2 class="section-title">Overriding Entities <a href="#"><i
class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
+ <p>
+ As we've seen above one named entity can replace or override an
older entity in the STM, e.g. <code>"Peet's"</code>
+ replaced <code>"Starbucks"</code> in our previous questions.
<b>The actual algorithm that governs this logic is one
+ of the most important part of STM implementation.</b> In human
conversations we perform this logic seemingly
+ subconsciously — but the computer algorithm to do it is not that
trivial. Let's see how it is done in NLPCraft.
+ </p>
+ <p>
+ One of the important supporting design decision is that an entity
can belong to one or more groups. You can think of
+ groups as types, or classes of entities (to be mathematically
precise these are the categories). The entity's
+ membership in such groups is what drives the rule of overriding.
+ </p>
+ <p>
+ Let's look at the specific example.
+ </p>
+ <p>
+ Consider a data model that defined 3 entities:
+ </p>
+ <ul>
+ <li>
+ <code>"sell"</code> (with synonym <code>"sales"</code>)
+ </li>
+ <li>
+ <code>"buy"</code> (with synonym <code>"purchase"</code>)
+ </li>
+ <li>
+ <code>"best_employee"</code> (with synonyms like
<code>"best"</code>, <code>"best employee"</code>, <code>"best
colleague"</code>)
+ </li>
+ </ul>
+ <p>
+ Our task is to support for following conversation:
+ </p>
+ <ul>
+ <li>
+ <code>"Give me the sales data"</code><br/>
+ We return sales information since we detected
<code>"sell"</code> entity by its synonym <code>"sales"</code>.
+ </li>
+ <li>
+ <code>"Who was the best?"</code><br/>
+ We return the best salesmen since we detected
<code>"best_employee"</code> and we should pick <code>"sell"</code> entity from
the STM.
+ </li>
+ <li>
+ <code>"OK, give me the purchasing report now."</code><br/>
+ This is a bit trickier. We should return general purchasing
data and not a best purchaser employee.
+ It feels counter-intuitive but we should NOT take
<code>"best_employee"</code> entity from STM and, in fact, we should remove it
from STM.
+ </li>
+ <li>
+ <code>"...and who's the best there?"</code><br/>
+ Now, we should return the best purchasing employee. We
detected <code>"best_employee"</code> entity and we should pick
<code>"buy"</code> entity from STM.
+ </li>
+ <li>
+ <code>"One more time - show me the general purchasing data
again"</code><br/>
+ Once again, we should return a general purchasing report and
ignore (and remove) <code>"best_employee"</code> from STM.
+ </li>
+ </ul>
+ </section>
+ <section>
+ <h2 class="section-title">Overriding Rule <a href="#"><i
class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
+ <p>
+ Here's the rule we developed at NLPCraft and have been
successfully using in various models:
+ </p>
+ <div class="bq success">
+ <div class="bq-idea-container">
+ <div><div>💡</div></div>
+ <div>
+ <b>Overriding Rule</b>
+ <p>
+ The entity will override other entity or entities in
STM that belong to the same group set or its superset.
+ </p>
+ </div>
+ </div>
+ </div>
+ <p>
+ In other words, an entity with a smaller group set (more specific
one) will override entity with the same
+ or larger group set (more generic one).
+ Let's consider an entity that belongs to the following groups:
<code>{G1, G2, G3}</code>. This entity:
+ </p>
+ <ul>
+ <li>
+ WILL override existing entity belonging to <code>{G1, G2,
G3}</code> groups (same set).
+ </li>
+ <li>
+ WILL override existing entity belonging to <code>{G1, G2, G3,
G4}</code> groups (superset).
+ </li>
+ <li>
+ WILL NOT override existing entity belonging to <code>{G1,
G2}</code> groups.
+ </li>
+ <li>
+ WIL NOT override existing entity belonging to <code>{G10,
G20}</code> groups.
+ </li>
+ </ul>
+ <p>
+ Let's come back to our sell/buy/best example. To interpret the
questions we've outlined above we need to
+ have the following 4 intents:
+ </p>
+ <ul>
+ <li><code>id=sale term={tok_id() == 'sale'}</code></li>
+ <li><code>id=best_sale_person term={tok_id() == 'sale'}
term={tok_id() == best_employee}</code></li>
+ <li><code>id=buy term={tok_id() == 'buy'}</code></li>
+ <li><code>id=buy_best_person term={tok_id() == 'buy'}
term={tok_id() == best_employee}</code></li>
+ </ul>
+ <p>
+ (this is actual <a href="/intent-matching.html">Intent Definition
Language</a> (IDL) used by NLPCraft -
+ <code>term</code> here is basically what's often referred to as a
slot in other systems).
+ </p>
+ <p>
+ We also need to properly configure groups for our entities (names
of the groups are arbitrary):
+ </p>
+ <ul>
+ <li>Entity <code>"sell"</code> belongs to group <b>A</b></li>
+ <li>Entity <code>"buy"</code> belongs to group <b>B</b></li>
+ <li>Entity <code>"best_employee"</code> belongs to groups <b>A</b>
and <b>B</b></li>
+ </ul>
+ <p>
+ Let’s run through our example again with this configuration:
+ </p>
+ <ul>
+ <li>
+ <code>"Give me the sales data"</code>
+ <ul>
+ <li>We detected entity from group <b>A</b>.</li>
+ <li>STM is empty at this point.</li>
+ <li>Return general sales report.</li>
+ <li>Store <code>"sell"</code> entity with group <b>A</b>
in STM.</li>
+ </ul>
+ </li>
+ <li>
+ <code>"Who was the best?"</code>
+ <ul>
+ <li>We detected entity belonging to groups <b>A</b> and
<b>B</b>.</li>
+ <li>STM has entity belonging to group <b>A</b>.</li>
+ <li><b>{A, B}</b> does NOT override <b>{A}</b>.</li>
+ <li>Return best salesmen report.</li>
+ <li>Store detected <code>"best_employee"</code>
entity.</li>
+ <li>STM now has two entities with <b>{A}</b> and <b>{A,
B}</b> group sets.</li>
+ </ul>
+ </li>
+ <li>
+ <code>"OK, give me the purchasing report now."</code>
+ <ul>
+ <li>We detected <code>"buy"</code> entity with group
<b>A</b>.</li>
+ <li>STM has two entities with <b>{A}</b> and <b>{A, B}</b>
group sets.</li>
+ <li><b>{A}</b> overrides both <b>{A}</b> and <b>{A,
B}</b>.</li>
+ <li>Return general purchasing report.</li>
+ <li>Store <code>"buy"</code> entity with group <b>A</b> in
STM.</li>
+ </ul>
+ </li>
+ </ul>
+ <p>
+ And so on... easy, huh 😇 In fact, the logic is indeed relatively
straightforward. It also follows
+ common sense where the logic produced by this rule matches the
expected human behavior.
+ </p>
+ </section>
+ <section>
+ <h2 class="section-title">Explicit Context Switch <a href="#"><i
class="top-link fas fa-fw fa-angle-double-up"></i></a></h2>
+ <p>
+ In some cases you may need to explicitly clear the conversation
STM without relying on algorithmic behavior.
+ It happens when current and new topic of the conversation share
some of the same entities. Although at first
+ it sounds counter-intuitive there are many examples of that in day
to day life.
+ </p>
+ <p>
+ Let’s look at this sample conversation:
+ </p>
+ <ul>
+ <li>
+ <b>Q</b>: <code>"What the weather in Tokyo?"</code><br/>
+ <b>A</b>: Current weather in Tokyo...
+ </li>
+ <li>
+ <b>Q</b>: <code>"Let’s do New York after all then!"</code><br/>
+ <b>A</b>: Without an explicit conversation reset we would
return current New York weather 🤔
+ </li>
+ </ul>
+ <p>
+ The second question was about going to New York (booking tickets,
etc.). In real life - your
+ counterpart will likely ask what you mean by "doing New York after
all" and you’ll have to explain
+ the abrupt change in the topic.
+ You can avoid this confusion by simply saying: "Enough about
weather! Let’s talk about this weekend plans" - after
+ which the second question becomes clear. That sentence is an
explicit context switch which you can also detect
+ in the NLPCraft model.
+ </p>
+ <p>
+ In NLPCraft you can also explicitly reset conversation context
through API or by switching the model on the request.
+ </p>
+ </section>
+ <section>
+ <h2 class="section-title">Summary <a href="#"><i class="top-link fas
fa-fw fa-angle-double-up"></i></a></h2>
+ <p>
+ Let’s collect all our thoughts on STM into a few bullet points:
+ </p>
+ <ul>
+ <li>
+ Missing entities in incomplete sentences can be auto-recalled
from STM.
+ </li>
+ <li>
+ Newly detected type/category entity is likely indicating the
change of topic.
+ </li>
+ <li>
+ The key property of STM is its short-time storage and
overriding rule.
+ </li>
+ <li>
+ The explicit context switch is an important mechanism.
+ </li>
+ </ul>
+ <div class="bq info">
+ <b>
+ Short-Term Memory
+ </b>
+ <p>
+ It is uncanny how properly implemented STM can make
conversational interface <b>feel like a normal human
+ conversation</b>. It allows to minimize the amount of
parasitic dialogs and Q&A driven interfaces
+ without unnecessarily complicating the implementation of such
systems.
+ </p>
+ </div>
+ </section>
</div>
<div class="col-md-2 third-column">
<ul class="side-nav">
