Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering

https://aclanthology.org/2020.acl-main.505/

Transcript

1. Annual Conference of the 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 Language modeling Approach From original BERT paper
3. 3 • Corpus • Friends Dialogue Transcript • Character Mining Project • Tasks • FriendsQA • Friends Reading Comprehension • Friends Emotion Detection • Friends Personality Detection
4. 4 Corpus • 10 seasons of the Friends show • Dialogue Example: From Friends transcript
5. 5 FriendsQA Only Annotated first 4 seasons From FriendsQA dataset
6. 6 Related question answering Tasks – 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 with Original 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 with transferred 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
15. 15 Results for Friends QA
16. 16 Analysis • Ablation Studies Method EM 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
19. 19 Analysis • Error Analysis
20. 20 Analysis • Error examples
21. 21 Analysis • Error examples
22. 22 Analysis • Error examples
23. 23 Analysis • FriendsQA Task Remained 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 novel transformer 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 • Figure out 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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