Hi,

1. 65k is quite small. You might need many (Read: MANY) iterations till the
perplexity stops dropping by significant amounts.

2. In Moses, I think you can try this--- Add 2 lines as below:

Under *feature* add this: NeuralLM factor=0 name=LM1 order=5
path=<path/to/neural/lm/file>

Under *weight *add this: LM1=0.5

I am not 100% sure but it should work.




On Mon, Sep 14, 2015 at 1:54 PM, Rajnath Patel <[email protected]>
wrote:

> Thanks for quick response.
>
> @Raj Dabre
> Corpus statistics as follows-
> Approx -65k sentences, 1200k words, 50k vocab.
> Please suggest, what size of corpus is enough for neural LM training?
>
> @Riko
> I will try with development set and more epochs as you suggested. Back-off
> LM you mean fall back to neural LM if its not found in n-gram model(Please
> correct if I got it wrong). If so, could you please suggest how to
> configure the same with moses.
>
> Thanks.
>
>
>
>> Message: 1
>> Date: Mon, 14 Sep 2015 01:56:14 +0900
>> From: Raj Dabre <[email protected]>
>> Subject: Re: [Moses-support] Performance issue with Neural LM for
>>         English-Hindi SMT
>> To: Rajnath Patel <[email protected]>
>> Cc: moses-support <[email protected]>
>> Message-ID:
>>         <CAB3gfjCGapWtYTheh6mKHhica7v7d=
>> [email protected]>
>> Content-Type: text/plain; charset="utf-8"
>>
>> Hi,
>> I have had a similar experience with NPLM.
>> Do you perhaps have a small corpus?
>>
>> On Sun, Sep 13, 2015 at 6:51 PM, Rajnath Patel <[email protected]>
>> wrote:
>>
>> > Hi all,
>> >
>> > I have tried Neural LM(nplm) with phrase based English-Hindi SMT, but
>> > translation quality is kind of not good as compared to n-gram LM(scores
>> are
>> > given below). I have trained LM for 3-gram and 5-gram with default
>> > setting(as mentioned on statmt.org/moses). Kindly suggest, If some one
>> > has tried the same English-Hindi SMT and got improved results. What may
>> be
>> > probable cause of degraded results?
>> >
>> > BLEU scores:
>> > n-gram(5-gram)=24.40
>> > neural-lm(5-gram)=11.30
>> > neural-lm(3-gram)=12.10
>> >
>> > Thank you.
>> >
>> > --
>> > Regards:
>> > Raj Nath Patel
>> >
>> > _______________________________________________
>> > Moses-support mailing list
>> > [email protected]
>> > http://mailman.mit.edu/mailman/listinfo/moses-support
>> >
>> >
>>
>>
>> --
>> Raj Dabre.
>> Doctoral Student,
>> Graduate School of Informatics,
>> Kyoto University.
>> CSE MTech, IITB., 2011-2014
>> -------------- next part --------------
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>>
>> ------------------------------
>>
>> Message: 2
>> Date: Sun, 13 Sep 2015 23:19:19 +0100
>> From: Rico Sennrich <[email protected]>
>> Subject: Re: [Moses-support] Performance issue with Neural LM for
>>         English-Hindi SMT
>> To: [email protected]
>> Message-ID: <[email protected]>
>> Content-Type: text/plain; charset="windows-1252"
>>
>> Hello Raj,
>>
>> Usually, nplm is used in addition to a back-off LM for best results.
>> That being said, your results indicate that nplm is performing poorly.
>> If you have little training data, a smaller vocabulary size and more
>> training epochs may be appropriate. I would advise to provide a
>> development set to the nplm training program so that you can track the
>> training progress, and compare perplexity with back-off models.
>>
>> best wishes,
>> Rico
>>
>> On 13/09/15 10:51, Rajnath Patel wrote:
>> > Hi all,
>> >
>> > I have tried Neural LM(nplm) with phrase based English-Hindi SMT, but
>> > translation quality is kind of not good as compared to n-gram
>> > LM(scores are given below). I have trained LM for 3-gram and 5-gram
>> > with default setting(as mentioned on statmt.org/moses
>> > <http://statmt.org/moses>). Kindly suggest, If some one has tried the
>> > same English-Hindi SMT and got improved results. What may be probable
>> > cause of degraded results?
>> >
>> > BLEU scores:
>> > n-gram(5-gram)=24.40
>> > neural-lm(5-gram)=11.30
>> > neural-lm(3-gram)=12.10
>> >
>> > Thank you.
>> >
>> > --
>> > Regards:
>> > Raj Nath Patel
>>
>>
>
>
> --
> Regards:
> राज नाथ पटेल/Raj Nath Patel
> KBCS dept.
> CDAC Mumbai.
> http://kbcs.in/
>



-- 
Raj Dabre.
Doctoral Student,
Graduate School of Informatics,
Kyoto University.
CSE MTech, IITB., 2011-2014
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