Papers with transformer-based framework
Discourse Understanding and Factual Consistency in Abstractive Summarization (2021.eacl-main)
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Saadia Gabriel, Antoine Bosselut, Jeff Da, Ari Holtzman, Jan Buys, Kyle Lo, Asli Celikyilmaz, Yejin Choi
| Challenge: | Existing abstractive summarization models often hallucinate information or generate factually incorrect summaries. |
| Approach: | They propose a general framework for abstractive summarization with factual consistency and distinct modeling of the narrative flow in an output summary. |
| Outcome: | The proposed framework generates abstracts with factual consistency and coherence significantly better than baselines. |
Evaluating the Performance of Transformer-based Language Models for Neuroatypical Language (2022.coling-1)
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| Challenge: | Difficulties with social aspects of language are among the hallmarks of autism spectrum disorder (ASD). |
| Approach: | They propose a transformer-based framework for identifying linguistic features associated with social aspects of communication using a corpus of conversations between adults with and without ASD and neurotypical conversational partners. |
| Outcome: | The proposed framework yields strong accuracy overall, but performance is significantly worse for the language of participants with ASD, suggesting they use a more diverse set of strategies for some social linguistic functions. |