评估印度语言的双语间语义解析
Evaluating Inter-Bilingual Semantic Parsing for Indian Languages
AI总结：
针对印度语言缺乏复杂语义解析数据集的问题，本文构建覆盖11种语言的双语间 Seq2seq 数据集 IE-SEMPARSE，并通过多策略评测验证其与现有多语言基准性能高度相关。
摘要：
尽管印度语言自然语言生成（IndicNLP）已取得显著进展，但在语义解析等复杂结构化任务方面仍缺乏数据集。这一紧迫缺口的一个原因是逻辑形式的复杂性，使英语到多语言的翻译变得困难。该过程涉及将逻辑形式、意图和槽位与翻译后的非结构化话语进行对齐。为解决此问题，我们提出了一个面向11种不同印度语言的双语间 Seq2seq 语义解析数据集 IE-SEMPARSE。我们强调了所提出任务的实用性，并在多种训练—测试策略下评估现有多语言 seq2seq 模型。实验表明，原始多语言语义解析数据集（如 mTOP、multilingual TOP 和 multiATIS++）的性能与我们提出的 IE-SEMPARSE 套件之间存在高度相关性。
英文摘要：
Despite significant progress in Natural Language Generation for Indian languages (IndicNLP), there is a lack of datasets around complex structured tasks such as semantic parsing. One reason for this imminent gap is the complexity of the logical form, which makes English to multilingual translation difficult. The process involves alignment of logical forms, intents and slots with translated unstructured utterance. To address this, we propose an Inter-bilingual Seq2seq Semantic parsing dataset IE-SEMPARSE for 11 distinct Indian languages. We highlight the proposed task's practicality, and evaluate existing multilingual seq2seq models across several train-test strategies. Our experiment reveals a high correlation across performance of original multilingual semantic parsing datasets (such as mTOP, multilingual TOP and multiATIS++) and our proposed IE-SEMPARSE suite.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
