The effect won't be very evident on simple sentences, I think it would be more effective on sentences where choice of words can decide the efficiency of translation. It's not about if "Watch out" could be " be careful" , it's about choosing words that can retain the urgency in "watch out". Sentiment information on original sentence can help in that.
On Thu, Feb 27, 2020, 23:47 Scoop Gracie <scoopgra...@gmail.com> wrote: > So, "Watch out!" Could become "Be careful"? > > On Thu, Feb 27, 2020, 10:13 Rajarshi Roychoudhury < > rroychoudhu...@gmail.com> wrote: > >> It is not just about minimizing loss of sentiment , it is about using >> that information for better translation. A very trivial example would be >> that for some situations , sentences can project a strong sentiment and >> simple translation may not always yield the best result. However if we can >> use the knowledge of the sentiment to choose the words , it might give >> better result. >> >> As far as the codes are concerned, I need to study the source code , or a >> detailed documentation for proposing a feasible solution. >> >> Best, >> Rajarshi >> >> >> >> On Thu, Feb 27, 2020, 23:21 Tino Didriksen <m...@tinodidriksen.com> >> wrote: >> >>> My first question would be, is this actually a problem for rule-based >>> machine translation? I am not a linguist, but given how RBMT works I can't >>> really see where sentiment would be lost in the process, especially >>> because Apertium is designed for related languages where sentiment is >>> mostly the same. But even for less related languages, it would be down to >>> the quality of the source language analysis. >>> >>> Beyond that, please learn how Apertium specifically works, not just RBMT >>> in general. http://wiki.apertium.org/wiki/Documentation is a good >>> start, but our IRC channel is the best place to ask technical questions. >>> >>> One major issue specific to Apertium is that the source information is >>> no longer available in the target generation step. >>> >>> E.g., since you mention English-Hindi, you could install >>> apertium-eng-hin and see how each part of the pipe works. We have >>> precompiled binaries common platforms. Again, see wiki and IRC. >>> >>> -- Tino Didriksen >>> >>> >>> On Thu, 27 Feb 2020 at 08:16, Rajarshi Roychoudhury < >>> rroychoudhu...@gmail.com> wrote: >>> >>>> Formally i present my idea in this form: >>>> From my understanding of RBMT , >>>> >>>> The RBMT system contains: >>>> >>>> - a *SL morphological analyser* - analyses a source language word >>>> and provides the morphological information; >>>> - a *SL parser* - is a syntax analyser which analyses source >>>> language sentences; >>>> - a *translator* - used to translate a source language word into >>>> the target language; >>>> - a *TL morphological generator* - works as a generator of >>>> appropriate target language words for the given grammatica information; >>>> - a *TL parser* - works as a composer of suitable target language >>>> sentences >>>> >>>> I propose a 6th component of the RBMT system: *sentiment based TL >>>> morphological generator* >>>> >>>> I propose that we do word level sentiment analysis of the source >>>> language and targeted language. For the time being i want to work on >>>> English-Hindi translation. We do not need a neural network based >>>> translation, however for getting the sentiment associated with each word we >>>> might use nltk,or develop a character level embedding to just find out the >>>> sentiment assosiated with each word,and form a dictionary out of it.I have >>>> written a paper on it,and received good results.So basically,during the >>>> final application development we will just have the dictionary,with no >>>> neural network dependencies. This can easily be done with Python.I just >>>> need a good corpus of English and Hindi words(the sentiment datasets are >>>> available online). >>>> >>>> The *sentiment based TL morphological generator *will generate the >>>> list of possible words,and we will take that word whose sentiment is >>>> closest to the source language word. >>>> This is a novel method that has probably not been applied before, and >>>> might generate better results. >>>> >>>> Please provide your valuable feedwork and suggest some necessary >>>> changes that needs to be made. >>>> Best, >>>> Rajarshi >>>> >>> _______________________________________________ >>> Apertium-stuff mailing list >>> Apertium-stuff@lists.sourceforge.net >>> https://lists.sourceforge.net/lists/listinfo/apertium-stuff >>> >> _______________________________________________ >> Apertium-stuff mailing list >> Apertium-stuff@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/apertium-stuff >> > _______________________________________________ > Apertium-stuff mailing list > Apertium-stuff@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/apertium-stuff >
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