2021/10/07 by Yuchen He, Zhuosheng Zhang, He, Yuchen +3 · 12 citations
Computer Science · #Artificial intelligence #Benchmark (surveying) #Comprehension #Computation and Language (cs.CL) #Computer science #FOS: Computer and information sciences #Linguistics #Natural Language Processing Techniques #Natural language processing #Parsing #Reading (process) #Reading comprehension #Speech and dialogue systems #Style (visual arts) #Task (project management) #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.2110.03269
published in arXiv (Cornell University) (Cornell University) · Accepted by PACLIC 2021
arxiv created 2021/10/07 · openalex publication_date 2021/10/07 · arxiv updated 2021/10/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Multi-party dialogue machine reading comprehension (MRC) raises an even more challenging understanding goal on dialogue with more than two involved speakers, compared with the traditional plain passage style MRC. To accurately perform the question-answering (QA) task according to such multi-party dialogue, models have to handle fundamentally different discourse relationships from common non-dialogue plain text, where discourse relations are supposed to connect two far apart utterances in a linguistics-motivated way.To further explore the role of such unusual discourse structure on the correlated QA task in terms of MRC, we propose the first multi-task model for jointly performing QA and discourse parsing (DP) on the multi-party dialogue MRC task. Our proposed model is evaluated on the latest benchmark Molweni, whose results indicate that training with complementary tasks indeed benefits not only QA task, but also DP task itself. We further find that the joint model is distinctly stronger when handling longer dialogues which again verifies the necessity of DP in the related MRC.