This week, I would like to give some thoughts about word senses and representation contextualization in machine translation.

I will start by explaining why I think the current way of writing about word senses in NLP is kind of misleading and why I think we should rather talk about contextualization rather than word sense disambiguation. Then, I will have a look at a paper that discusses the word-sense disambiguation capabilities of representations in machine translation and try to interpret it in a slightly different way than the authors do.

It seems to me that when someone talks about word senses and the need to disambiguate them, the implicit underlying model of communication is the following:

• A wants to communicate a precise sense and puts it into a suitable envelope and seals the envelope. (The envelope is a word.)

• She does the same with every single word and passes a pile of envelopes (a sentence) to B.

• B takes the envelopes, which he cannot unseal. He carefully goes over the envelopes and based on how all the envelopes look like and how they were ordered, he tries to guess what is in the envelopes. For some envelopes, it is clear what is inside, for others, an educated guess about the content is required.

• B thinks he knows what A wanted to tell him.

The penultimate step is the word sense disambiguation. In the words of this poor metaphor, my objection is the following: How can B even know something is in the envelopes? B can know that he puts something in the envelopes, but how can he know that others do it as well if he cannot unseal the envelopes? How can he know others put the same things in the envelopes?

The traditional formulation of word-sense disambiguation assumes that sense is something primary (although not directly observable) and senses are an inherent property of (or behind) words. Words are then necessarily ambiguous because multiple senses can be represented by a single word (put in the same envelope). Unlike words, word senses can never be observed and unlike other theoretical entities (like elementary particles in physics) you can hardly imagine a scientific experiment that would prove their existence. Talking about something that cannot be observed or proved is something researchers should avoid.

Luckily, we can reformulate the thing with word senses in a different way that eliminates this problem. When words do not have any sense on their own and get meaning only when used in context, it poses no surprise that when used in a different context, the meaning can be different as well. Word-sense disambiguation can be reformulated as classifying the (outer) context rather than a (inner) sense. Classes of (similar and dissimilar) contexts are something that without doubts exists. Word sense disambiguation is thus giving interpretable names to distinct classes of contexts in which words can be used. Disambiguation is contextualization.

This nitpicky theoretical introduction gets me to what I wanted to talk about today. It is a paper titled Encoders Help You Disambiguate Word Senses in Neural Machine Translation published at this year’s EMNLP.

It is an exploratory paper that evaluates whether and how hidden states (internal representations) in neural networks for machine translation are useful for word-sense disambiguation. Long story short: they are very useful. Without proper context, the models can guess the sense (= context class) with accuracy between 63% and 68% when utilizing the internal states, it gets accuracy between 91% and 97%.

They compared models based on recurrent networks and Transformer architectures and also two target languages: German and French (with English source because the word-sense disambiguation data only exist for English). The results show that translation in German knows more about word senses, perhaps there are more semantic mismatches between words than between English and French. Also, the Transformer encoders do a better job than recurrent encoders. If we view the word sense disambiguation as introducing more context (instead of specifying the meaning by removing unused sense), this makes perfect sense.

The slightly surprising result of the paper is that representations from the Transformer decoder perform worse than the encoder. In the paper, they call it a surprise because they assume word sense must be entirely clear at the end of the translation process.

I have some doubts about it. First, the experiment seems to be a little methodologically skewed, because the decoder is provided with the ground-truth prefix of the target sentence and this target sentence context can clarify what in what sense the source-sentence word is used (especially when they sum subword representations), so the accuracy might be overestimated. Second, the last (the only tested) decoder layer can already live in the world of ambiguous target language words, so it might be already obfuscated by the target language ambiguities.

The paper concludes that the hidden states contain information that is useful for word-sense disambiguation, but I would not be afraid of distilling more interesting hypotheses from the paper.

If we view word-sense disambiguation as context classification, we can conclude that the encoder states are better contextualized — managed to gather the context that is relevant to getting the meaning of the word. The model needs to do also the reverse process: decontextualize the information to ultimately get a distribution over (ambiguous if you want) target words. Differences between the recurrent and Transformer networks only show that the models learned a different dynamics of contextualizing and decontextualizing the representation. Since the self-attentive layers consider all possible word combinations, I believe it is easier for them to do both contextualization and decontextualization more quickly.

@inproceedings{tang-etal-2019-encoders,
title = "Encoders Help You Disambiguate Word Senses in Neural Machine Translation",
author = "Tang, Gongbo and Sennrich, Rico and Nivre, Joakim",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",