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 -------------- > An HTML attachment was scrubbed... > URL: > http://mailman.mit.edu/mailman/private/moses-support/attachments/20150913/7fa15fdd/attachment-0001.html > > ------------------------------ > > 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/
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