Publication

Moving Down the Long Tail of Word Sense Disambiguation with Gloss Informed Bi-encoders

Association for Computational Linguistics (ACL)


Abstract

A major obstacle in Word Sense Disambiguation (WSD) is that word senses are not uniformly distributed, causing existing models to generally perform poorly on senses that are either rare or unseen during training. We propose a bi-encoder model that independently embeds (1) the target word with its surrounding context and (2) the dictionary definition, or gloss, of each sense. The encoders are jointly optimized in the same representation space, so that sense disambiguation can be performed by finding the nearest sense embedding for each target word embedding. Our system outperforms previous state-of-the-art models on English all-words WSD; these gains predominantly come from improved performance on rare senses, leading to a 31.1% error reduction on less frequent senses over prior work. This demonstrates that rare senses can be more effectively disambiguated by modeling their definitions.

Related Publications

All Publications

Spatially Aware Multimodal Transformers for TextVQA

Yash Kant, Dhruv Batra, Peter Anderson, Alexander Schwing, Devi Parikh, Jiasen Lu, Harsh Agrawal

ECCV - August 23, 2020

Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline

Vishvak Murahari, Dhruv Batra, Devi Parikh, Abhishek Das

ECCV - August 23, 2020

Word-level Speech Recognition with a Letter to Word Encoder

Ronan Collobert, Awni Hannun, Gabriel Synnaeve

ICML - July 13, 2020

Compositionality and Generalization in Emergent Languages

Rahma Chaabouni, Eugene Kharitonov, Diane Bouchacourt, Emmanuel Dupoux, Marco Baroni

ACL - July 4, 2020

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy