Learning to Describe Unknown Phrases with Local and Global Contexts

Proceedings of NAACL-HLT 2019, pages 3467–3476 Minneapolis, Minnesota, June 2 - June 7, 2019. c⃝2019 Association for Computational Linguistics 3467 Learning to Describe Unknown Phrases with Local and Global Contexts Shonosuke Ishiwatari† Hiroaki Hayashi‡ Naoki Yoshinaga§ Graham Neubig‡ Shoetsu Sato† Masashi Toyoda§ Masaru Kitsuregawa¶§ † The University of Tokyo ‡ Carnegie Mellon University § Institute of Industrial Science, the University of Tokyo ¶ National Institute of Informatics †§¶{ishiwatari,ynaga,shoetsu,toyoda,kitsure}@tkl.iis.u-tokyo.ac.jp ‡{hiroakih, gneubig}@cs.cmu.edu Abstract When reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging enti- ties. If we humans cannot figure out the mean- ing of those expressions from the immediate local context, we consult dictionaries for def- initions or search documents or the web to find other global context to help in interpre- tation. Can machines help us do this work? Which type of context is more important for machines to solve the problem? To answer these questions, we undertake a task of de- scribing a given phrase in natural language based on its local and global contexts. To solve this task, we propose a neural description model that consists of two context encoders and a description decoder. In contrast to the existing methods for non-standard English ex- planation (Ni and Wang, 2017) and defini- tion generation (Noraset et al., 2017; Gadetsky et al., 2018), our model appropriately takes im- portant clues from both local and global con- texts. Experimental results on three existing datasets (including WordNet, Oxford and Ur- ban Dictionaries) and a dataset newly created from Wikipedia demonstrate the effectiveness of our method over previous work. 1 Introduction When we read news text with emerging entities, text in unfamiliar domains, or text in foreign lan- guages, we often encounter expressions (words or phrases) whose senses we do not understand. In such cases, we may first try to figure out the mean- ings of those expressions by reading the surround- ing words (local context) carefully. Failing to do so, we may consult dictionaries, and in the case of polysemous words, choose an appropriate mean- ing based on the context. Learning novel word senses via dictionary definitions is known to be Figure 1: Local & Global Context-aware Description generator (LOG-CaD). more effective than contextual guessing (Fraser, 1998; Chen, 2012). However, very often, hand- crafted dictionaries do not contain definitions of expressions that are rarely used or newly created. Ultimately, we may need to read through the entire document or even search the web to find other oc- curances of the expression (global context) so that we can guess its meaning. Can machines help us do this work? Ni and Wang (2017) have proposed a task of generating a definition for a phrase given its local context. However, they follow the strict assumption that the target phrase is newly emerged and there is only a single local context available for the phrase, which makes the task of generating an accurate and co- herent definition …

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