Papers with Teacher Forcing
Hybrid Autoregressive-Diffusion Model for Real-Time Sign Language Production (2026.acl-long)
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| Challenge: | Existing approaches to sign language production use autoregressive or diffusion models that generate one-by-one output tokens but suffer from exposure bias during inference. |
| Approach: | They propose a hybrid autoregressive-diffusion model that combines iterative refinement and sequential dependency modeling for Sign Language production. |
| Outcome: | The proposed model improves sign language production quality and real-time efficiency on PHOENIX14T and How2Sign. |
Exploring Continual Learning for Code Generation Models (2023.acl-short)
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Prateek Yadav, Qing Sun, Hantian Ding, Xiaopeng Li, Dejiao Zhang, Ming Tan, Parminder Bhatia, Xiaofei Ma, Ramesh Nallapati, Murali Krishna Ramanathan, Mohit Bansal, Bing Xiang
| Challenge: | Large-scale code generation models such as Copilot and CodeT5 are expensive to train and re-train. |
| Approach: | They propose a benchmark for Continual Learning (CL) that covers a wide range of tasks with different input and output programming languages. |
| Outcome: | The proposed method improves on Prompt Pooling with Teacher Forcing, which suffers catastrophic forgetting due to stark distribution shifts in coding tasks. |
Incorporating Graph Information in Transformer-based AMR Parsing (2023.findings-acl)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic graph abstraction for text representations. |
| Approach: | They propose a model and method that incorporates graph information into the learned representations of AMR by word-to-node alignment. |
| Outcome: | The proposed model improves AMR parsing performance by embedding graph information into the encoder at training time. |
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning (2023.emnlp-main)
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| Challenge: | Query-focused Summarization (QfS) is a system that generates summaries from document(s) based on a query. |
| Approach: | They propose a Query-focused Summarization approach that uses a generalization of Reinforcement Learning (RL) for Natural Language Generation and a better semantic similarity reward. |
| Outcome: | The proposed approach improves on the ROUGE-L metric and in a benchmark dataset. |