# Learning to Represent Words in Context with Multilingual Supervision

arXiv: https://arxiv.org/abs/1511.04623 · HTML: https://mlsanity.com/paper/1511.04623/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2015.

## Built on by (most cited first, 1 of 1)

- [Leveraging Natural Supervision for Language Representation Learning and Generation](https://mlsanity.com/paper/2207.10617/) (2022)

## Abstract (arXiv)

We present a neural network architecture based on bidirectional LSTMs to compute representations of words in the sentential contexts. These context-sensitive word representations are suitable for, e.g., distinguishing different word senses and other context-modulated variations in meaning. To learn the parameters of our model, we use cross-lingual supervision, hypothesizing that a good representation of a word in context will be one that is sufficient for selecting the correct translation into a second language. We evaluate the quality of our representations as features in three downstream tasks: prediction of semantic supersenses (which assign nouns and verbs into a few dozen semantic classes), low resource machine translation, and a lexical substitution task, and obtain state-of-the-art results on all of these.