Papers with Sequence-to-Sequence
Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction (2020.findings-emnlp)
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Ranran Haoran Zhang, Qianying Liu, Aysa Xuemo Fan, Heng Ji, Daojian Zeng, Fei Cheng, Daisuke Kawahara, Sadao Kurohashi
| Challenge: | Existing methods to extract relation triplets from plain text introduce exposure bias . prior work has focused on pipeline methods that ignore intrinsic interactions between subtasks and propagate classification errors through the tasks. |
| Approach: | They propose a model that reduces the decoding length to three within a triplet and removes the order among triplets. |
| Outcome: | The proposed model overfits to both datasets while showing better generalization. |
Unleashing the True Potential of Sequence-to-Sequence Models for Sequence Tagging and Structure Parsing (2023.tacl-1)
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| Challenge: | Sequence-to-Sequence (S2S) models have been successful on text generation tasks . however, learning complex structures with S2S models remains challenging . |
| Approach: | They propose to use constrained decoding to model part-of-speech tagging, named entity recognition, constituency, and dependency parsing tasks with 3 lexically diverse linearization schemas and corresponding constrained coding methods. |
| Outcome: | The proposed methods outperform the state-of-the-art on four core tasks. |
Extending Neural Generative Conversational Model using External Knowledge Sources (D18-1)
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| Challenge: | Existing generative dialogue models lack coherence and are content poor . however, current models lack the capacity to handle large unstructured knowledge sources. |
| Approach: | They propose an architecture to incorporate unstructured knowledge sources to enhance the next utterance prediction in chit-chat type of generative dialogue models. |
| Outcome: | The proposed architecture improves the next utterance prediction in chit-chat type of generative dialogue models by incorporating external knowledge from Wikipedia summaries and the NELL knowledge base. |
NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation (D18-1)
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| Challenge: | Sequence-to-Sequence models favor short generic responses . however, the model is not suitable for modeling dialogues . |
| Approach: | They propose a model that connects preceding and following conversations to a prior distribution to avoid non-differentiability of discrete natural language tokens. |
| Outcome: | The proposed model is highly efficient in learning the backbone of human-computer communications, but favors short generic responses. |
Cognate Detection for Historical Language Reconstruction of Proto-Sabean Languages: the Case of Ge’ez, Tigrinya, and Amharic (2025.coling-main)
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| Challenge: | As languages evolve, we risk losing ancestral languages. |
| Approach: | They propose to use cognates to reconstruct proto-languages from cognates in child languages that have likely evolved from the same word in the proto-linguistics. |
| Outcome: | The proposed method is based on automatic cognate detection and in-context learning with GPT-4o to generate the proto-language from the cognates and use Sequence-to-Sequence models. |