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Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering
This document summarizes a research paper that proposes a new transformer model for span-based question answering on dialogue transcripts. The model is pretrained on tasks like masked language modeling at the token and utterance level, as well as utterance order prediction, using the Friends TV show transcript corpus. It is then fine-tuned jointly on two tasks: utterance ID prediction and token span prediction. Evaluation on the FriendsQA dataset shows the proposed model outperforms BERT and RoBERTa baselines. However, analysis finds the model still struggles with inference in dialogues and representing speakers.
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Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering
- 1. Annual Conference ofthe Associa1on for Computa1onal Linguis1cs Changmao Li and Jinho D. Choi Emory University Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering
- 2. 2 Recent Contextual Languagemodeling Approach From original BERT paper
- 3. 3 • Corpus • FriendsDialogue Transcript • Character Mining Project • Tasks • FriendsQA • Friends Reading Comprehension • Friends Emotion Detection • Friends Personality Detection
- 4. 4 Corpus • 10 seasonsof the Friends show • Dialogue Example: From Friends transcript
- 5. 5 FriendsQA Only Annotated first4 seasons From FriendsQA dataset
- 6. 6 Related question answeringTasks – general domain datasets • SQuAD 1.0 • SQuAD 2.0 • MS Marco • TRIVIAQA • NEWSQA • Narra1veQA – Mul0-turn ques0on answering datasets • SQA • QuAC • CoQA • CQA – Dialogue based ques0on answering datasets • Dream
- 7. 7 Approach • Problem withOriginal Transformer Based language modeling Approach for Dialogue • They pretrained on the formal wri*ng not dialogue based corpus • Simply concatenaDng all the dialogue uEerances into whole context as input
- 8. 8 Approach • Pretraining withtransferred BERT/RoBERTa weights – Token level masked language model – Utterance level masked language model – Utterance order prediction • Fine-tuning – Joint learning of two tasks
- 9. 9 Pretraining • Stage 1:Token level Masked Language Model
- 10. 10 Pretraining • Stage 2:UGerance Level Masked language model
- 11. 11 Pretraining • Stage 3:Utterance order prediction
- 12. 12 Fine-tuning • FriendsQA task –The utterance ID prediction – The token span prediction
- 13. 13 Experiments • FriendsQA task –Chronological Data Split
- 14. 14 Experiments • FriendsQA task –EvaluaDon Metrics: • EM: exact match – Check if the predic-on and gold answer are the exactly same • SM: Span-based Match – Each answer is treated as bag-of-words – Compute macro-average F1 score • UM: u>erance match – checks if the predic-on resides within the same u?erance as the gold answer span
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- 16. 16 Analysis • Ablation Studies MethodEM SM UM BERTpre with uid_loss 45.7(±0.8) 61.1(±0.8) 71.5(±0.5) BERTpre without uid_loss 45.6(±0.9) 61.2(±0.7) 71.3(±0.6) BERTpre+ulm with uid_loss 46.2(±1.1) 62.4(±1.2) 72.5(±0.8) BERTpre+ulm without uid_loss 45.7(±0.9) 61.8(±0.9) 71.8(±0.5) BERTpre+ulm+uop with uid_loss 46.8(±1.3) 63.1(±1.1) 73.3(±0.7) BERTpre+ulm+uop without uid_loss 45.6(±0.9) 61.7(±0.7) 71.7(±0.6) RoBERTapre with uid_loss 52.8(±0.9) 68.7(±0.8) 81.9(±0.5) RoBERTapre without uid_loss 52.6(±0.7) 68.6(±0.6) 81.7(±0.7) RoBERTapre+ulm with uid_loss 53.2(±0.6) 69.2(±0.7) 82.4(±0.5) RoBERTapre+ulm without uid_loss 52.9(±0.8) 68.7(±1.1) 81.7(±0.6) RoBERTapre+ulm+uop with uid_loss 53.5(±0.7) 69.6(±0.8) 82.7(±0.5) RoBERTapre+ulm+uop without uid_loss 52.5(±0.8) 68.8(±0.5) 81.9(±0.7)
- 17. 17 Analysis • FriendsQA Tasks –QuesDon type analysis
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- 23. 23 Analysis • FriendsQA TaskRemained Challenges – Inference in the dialogue? • Still mainly doing pattern matching. • In some cases, the utterance id prediction let model forcedly learn the right utterance of an answer span. – Deal with speakers and mentions? • Adding the speakers into the vocabulary cannot improve the results.
- 24. 24 Conclusion • A noveltransformer approach that interprets hierarchical contexts in multiparty dialogue. • Evaluated on FriendsQA task and outperforms BERT and RoBERTa. • Although the model shows no help to other character mining tasks, it still gives promising idea for future studies.
- 25. 25 List of Contributions •New pre-training tasks are introduced to improve the quality of both token-level and u;erance-level embeddings generated by the transformers, that be;er suit to handle dialogue contexts. • A new mul@-task learning approach is proposed to fine-tune the language model for span-based QA that takes full advantage of the hierarchical embeddings created from the pre-training. • The approach outperforms the previous state-of-the- art models using BERT and RoBERTa on the span-based QA task using dialogues as evidence documents.
- 26. 26 Future work • Figureout how to represent speakers and menQons in the dialogue. • Figure out how to inference in the dialogue. • Design new advanced dialogue language model that can fit for all tasks.
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