| Challenge: | Neural sequence generation is typically performed token-by-token and left-to-right. |
| Approach: | They propose to use placeholder tokens to make the sequence generation process bidirectional by taking past and future tokens into consideration when generating the actual output token. |
| Outcome: | The proposed approach outperforms baselines on two conversational tasks by a large margin. |
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| Challenge: | Prior work on language model pre-training explored different architectures and learning objectives, but differences in data, hyperparameters and evaluation make a principled comparison difficult. |
| Approach: | They propose a framework that generalizes prior approaches to pre-training language models by focusing on bidirectionality and controlling each of them separately. |
| Outcome: | The proposed framework generalizes prior approaches including fully unidirectional models like GPT, fully bidirectional models and hybrid models like CM3 and prefix LM. |
Predicting Through Generation: Why Generation Is Better for Prediction (2025.acl-long)
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Md Kowsher, Nusrat Jahan Prottasha, Prakash Bhat, Chun-Nam Yu, Mojtaba Soltanalian, Ivan Garibay, Ozlem Garibay, Chen Chen, Niloofar Yousefi
| Challenge: | Large Language Models (LLMs) are increasingly used for predictive tasks such as classification and regression. |
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Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling (2022.emnlp-main)
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| Challenge: | Existing studies on dialogue modeling use pre-trained language models to encode dialogue history as successive tokens, which is insufficient in capturing the temporal characteristics of dialogues. |
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Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing (P18-1)
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| Challenge: | Existing methods for semantic parsing are difficult to design and learn, especially in wideopen domains. |
| Approach: | They propose a neural semantic parsing approach which models semantic par- sing as an end-to-end semantic graph generation process. |
| Outcome: | The proposed model achieves state-of-the-art performance on Overnight dataset and gets competitive performance on Geo and Atis datasets. |
Let’s do it “again”: A First Computational Approach to Detecting Adverbial Presupposition Triggers (P18-1)
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| Challenge: | a novel task of predicting adverbial presupposition triggers is useful for natural language generation . a focus is on a new attention mechanism for predicting presuposition trigger . |
| Approach: | They propose a new attention mechanism for predicting adverbial presupposition triggers . they propose to augment a baseline neural network without additional trainable parameters . |
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Mind Your Special Tokens! On the Importance of Dedicated Sequence-End Tokens in Vision-Language Embedding Models (2026.eacl-short)
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| Challenge: | Large Vision-Language Models (LVLMs) are highly sensitive to end-of-input artifacts in fine-tuning and inference data, e.g., whether input sequences end with punctuation or newline characters. |
| Approach: | They propose to convert generative LVLMs into vision-language encoders via contrastive learning objectives and use supervised contrastive objectives to train them. |
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Token-Level Self-Evolution Training for Sequence-to-Sequence Learning (2023.acl-short)
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| Challenge: | Adaptive training approaches do not consider the variation of learning difficulty in different training steps, making the learning deterministic and sub-optimal. |
| Approach: | They propose a dynamic token-level self-evolution training method that reweighs the training losses of different target tokens based on priors. |
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A Framework for Bidirectional Decoding: Case Study in Morphological Inflection (2023.findings-emnlp)
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| Challenge: | Existing encoder-decoders that generate sequences from left to right are prone to errors due to the "snowballing" effect. |
| Approach: | They propose a transformer-based encoder-decoder framework that produces sequences from the "outside-in" they argue that this approach is more principled than prior bidirectional decoders . |
| Outcome: | The proposed model beats the current system by over 4.7 and 2.7 points in accuracy on 2022 and 2023 tasks. |
The Importance of Generation Order in Language Modeling (D18-1)
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| Challenge: | Neural language models are universally autoregressive, generating sentences one token at a time from left to right. |
| Approach: | They propose a two-pass language model that generates partially-filled sentences and fills in missing tokens. |
| Outcome: | The proposed model produces partially-filled sentences and fills in missing tokens. |
Zero-Shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens (N18-1)
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| Challenge: | Recent work has used attention weights to visualize the focus of neural models in input data. |
| Approach: | They propose to use attention-based visualization techniques to infer token-level labels from a network trained only on sentence-level labeling. |
| Outcome: | The proposed approach outperforms gradient-based methods on four datasets and is expected to outperfect supervised methods. |