Ann Copestake — accepted AI-conference papers

2021

TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling

Huiyuan Xie, Zhenghao Liu, Chenyan Xiong, Zhiyuan Liu, Ann Copestake

EMNLP 2021finding

Human conversations naturally evolve around different topics and fluently move between them. In research on dialog systems, the ability to actively and smoothly transition to new topics is often ignored. In this paper we introduce TIAGE, a new topic-shift aware dialog benchmark constructed utilizing…

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2020

Morphologically Aware Word-Level Translation

Paula Czarnowska, Sebastian Ruder, Ryan Cotterell, Ann Copestake

COLING 2020main

We propose a novel morphologically aware probability model for bilingual lexicon induction, which jointly models lexeme translation and inflectional morphology in a structured way. Our model exploits the basic linguistic intuition that the lexeme is the key lexical unit of meaning, while inflectiona…

Cited by 2 Source ↗PDF ↗Scholar ↗