Papers with general language model
CoCoLM: Complex Commonsense Enhanced Language Model with Discourse Relations (2022.findings-acl)
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| Challenge: | Large-scale pre-trained language models have demonstrated strong knowledge representation ability, but struggle with complex commonsense knowledge that involves multiple eventualities. |
| Approach: | They propose to help pre-trained language models better incorporate complex commonsense knowledge that involves multiple eventualities. |
| Outcome: | The proposed model can learn to use the memorized knowledge for different tasks and achieve outstanding performance on many downstream natural language processing (NLP) tasks. |
Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues (N19-1)
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| Challenge: | Recent surge of text-based online counseling applications enables us to collect and analyze interactions between counselors and clients. |
| Approach: | They develop a pre-trained conversation model that learns to classify client utterances into categories that help counselors in diagnosing client status and predicting counseling outcome. |
| Outcome: | The proposed model outperforms state-of-the-art comparison models and shows expected linguistic patterns for each category. |
CDLM: Cross-Document Language Modeling (2021.findings-emnlp)
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| Challenge: | Existing language models (LMs) provide powerful representations for internal text structure, but there are important applications for multi-text tasks. |
| Approach: | They propose a pretraining approach that incorporates two key ideas into the masked language modeling objective. |
| Outcome: | The proposed model improves over existing models and sets of long-range transformers and can be easily applied to multiple multi-text tasks. |
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding (2024.naacl-long)
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| Challenge: | End-to-end learning models require large volume of speech data with intent labels . however, models are sensitive to inconsistencies between training and evaluation conditions . |
| Approach: | They propose a module-based approach to learn intent in a noisy-channel model . they correlate error patterns between clean and noisy ASR transcripts . |
| Outcome: | The proposed method outperforms existing methods and improves in noisy environments. |
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models (2022.naacl-main)
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Peter West, Chandra Bhagavatula, Jack Hessel, Jena Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, Yejin Choi
| Challenge: | Prior studies suggested pre-trained language models possess limited understanding of commonsense knowledge despite otherwise stellar performance on leaderboards. |
| Approach: | They propose a framework that uses larger models to teach smaller models by distilling knowledge symbolically as text in addition to the neural model. |
| Outcome: | The proposed framework is based on a general language model teacher's commonsense knowledge graphs and a neural commonsensing model surpassing the teacher model's in all three criteria. |