Hierarchical Context Tagging for Utterance Rewriting

• URL: http://arxiv.org/abs/2206.11218v1
• Date: Wed, 22 Jun 2022 17:09:34 GMT
• Title: Hierarchical Context Tagging for Utterance Rewriting
• Authors: Lisa Jin, Linfeng Song, Lifeng Jin, Dong Yu, Daniel Gildea
• Abstract summary: Methods that tag rather than linearly generate sequences have proven stronger in both in- and out-of-domain rewriting settings. We propose a hierarchical context tagger that mitigates this issue by predicting slotted rules. Experiments on several benchmarks show that HCT can outperform state-of-the-art rewriting systems by 2 BLEU points.
• Score: 51.251400047377324
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Utterance rewriting aims to recover coreferences and omitted information from the latest turn of a multi-turn dialogue. Recently, methods that tag rather than linearly generate sequences have proven stronger in both in- and out-of-domain rewriting settings. This is due to a tagger's smaller search space as it can only copy tokens from the dialogue context. However, these methods may suffer from low coverage when phrases that must be added to a source utterance cannot be covered by a single context span. This can occur in languages like English that introduce tokens such as prepositions into the rewrite for grammaticality. We propose a hierarchical context tagger (HCT) that mitigates this issue by predicting slotted rules (e.g., "besides_") whose slots are later filled with context spans. HCT (i) tags the source string with token-level edit actions and slotted rules and (ii) fills in the resulting rule slots with spans from the dialogue context. This rule tagging allows HCT to add out-of-context tokens and multiple spans at once; we further cluster the rules to truncate the long tail of the rule distribution. Experiments on several benchmarks show that HCT can outperform state-of-the-art rewriting systems by ~2 BLEU points.

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