Focus Paper: Discriminative Modeling of Extraction Sets for Machine Translation
Yesterday during the seminar we talked about a good variety of topics relevant to machine translation. First we talked about Inversion Transduction Grammars (ITG) and how they work, namely the permutation possibilities they can generate, and how they help solve the word alignment problem which is a fundamental component in MT. Dong read a paper about statistical phrase-based translation which led to discussion on some of the earlier papers in MT.
Another big component in the discussion was supervised versus unsupervised word alignment models, and I feel like in general there were more useful things said about unsupervised approaches, one of the reasons being there may be too many dependencies in a system if a supervised approach is used; unsupervised approaches by nature seem more flexible. Daniel and I discussed the paper we read, which provided an unsupervised model to improve alignment for tree transducer-based syntactic machine translation models. I found the idea of a Markov walk along a parse tree to be pretty cool and there was a nice drop in AER, but the authors could not say anything about the fact that it improves translation since there was no incorporation into a full system. Dhananjay read a paper on MIRA which led to some interesting talk about multi-class labeling problems and structure prediction problems. Chris also brought up some points about the nice DP method used in the focus paper.
At the end we talked about several things, including maybe why the paper Daniel and I read did not experiment with a full system, and this lead to brief discussions about what code is open-source and why things may be difficult to share amongst the research community. We also discussed some limitations of the focus paper, a major one Kevin noticed was that sentences past length 40 were ignored, which actually accounts for roughly 20% of all sentences in news articles namely the Newswire data used in training and evaluation. It turns out this is kind of unsettling, since sentence stability and BLEU scores correlate significantly with length, and so limiting the data used may suggest something about the performance of the model on the data left out.
Overall I enjoyed the discussion as my purpose for taking the class is to some of the more relevant and complicated problems in NLP. I may have skipped over some conversation topics, so feel free to comment - I am adding them as I remember.