Machine Learning (Theory)

7/23/2005

Interesting papers at ACL

Tags: Language,Papers hal@ 8:59 am

A recent discussion indicated that one goal of this blog might be to allow people to post comments about recent papers that they liked. I think this could potentially be very useful, especially for those with diverse interests but only finite time to read through conference proceedings. ACL 2005 recently completed, and here are four papers from that conference that I thought were either good or perhaps of interest to a machine learning audience.

David Chiang, A Hierarchical Phrase-Based Model for Statistical Machine Translation. (Best paper award.) This paper takes the standard phrase-based MT model that is popular in our field (basically, translate a sentence by individually translating phrases and reordering them according to a complicated statistical model) and extends it to take into account hierarchy in phrases, so that you can learn things like “X ‘s Y” -> “Y de X” in chinese, where X and Y are arbitrary phrases. This takes a step toward linguistic syntax for MT, which our group is working strongly on, but doesn’t require any linguists to sit down and write out grammars or parse sentences.

Rie Kubota Ando and Tong Zhang, A High-Performance Semi-Supervised Learning Method for Text Chunking. This is more of a machine learning style paper, where they improve a sequence labeling task by augmenting it with models from related tasks for which data is free. I.e., I might train a model that, given a context with a missing word, will predict the word (eg., “The ____ gave a speech” might want you to insert “president”.) By doing so, you can use these other models to give additional useful information to your main task.

Noah A. Smith and Jason Eisner, Contrastive Estimation: Training Log-Linear Models on Unlabeled Data. This paper talks about training sequence labeling models in an unsupervised fashion, basically by contrasting what the model does on the correct string with what the model does on a corrupted version of the string. They get significantly better results than just by using EM in an HMM, and the idea is pretty nice.

Patrick Pantel, Inducing Ontological Co-occurrence Vectors. This is a pretty neat idea (though I’m biased — Patrick is a friend) where one attempts to come up with feature vectors that describe nodes in a semantic hierarchy (ontology) that could enable you to figure out where to insert new words that are not in your ontology. The results are pretty good, and the method is fairly simple; I’d imagine that a more complex model/learning framework could improve the model even further.

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