Papers with ROCStories
VLIS: Unimodal Language Models Guide Multimodal Language Generation (2023.emnlp-main)
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| Challenge: | Existing vision-language models face challenges in tasks that require complex linguistic understanding. |
| Approach: | They propose a framework that combines visual conditioning and linguistic understanding of unimodal text-only language models without further training to improve vision-language models. |
| Outcome: | The proposed framework improves vision-language models on diverse tasks including commonsense understanding and complex text generation. |
Unsupervised Enrichment of Persona-grounded Dialog with Background Stories (2021.acl-short)
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| Challenge: | Existing dialog models do not contain such narratives, so we propose a gradient-based rewriting technique to enrich dialog personas with relevant background events. |
| Approach: | They propose to use existing dialog datasets to enrich dialog responses with 'background stories' based on a gradient-based rewriting technique which encourages the generated response to be fluent with the dialog history, minimally different from the retrieved story, and consistent with the original persona. |
| Outcome: | The proposed method generates responses that are more diverse and human-like compared to outputs from existing dialog models. |
Scene Restoring for Narrative Machine Reading Comprehension (2020.emnlp-main)
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| Challenge: | Narrative passages describe a chain of events, which helps the machine understand the passage comprehensively. |
| Approach: | They propose a method to let machine read narrative passages with their prior knowledge . they build a scene graph using Atomic as external knowledge and encode it with GDIN . |
| Outcome: | The proposed method achieves state-of-the-art on a Story Cloze Test and CosmosQA datasets. |
Improving Machine Reading Comprehension with General Reading Strategies (N19-1)
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| Challenge: | Recent studies have shown that reading strategies improve comprehension levels for readers lacking adequate prior knowledge. |
| Approach: | They propose three general strategies to improve machine reading comprehension (MRC) by fine-tuning a pre-trained model with strategies and a target task. |
| Outcome: | The proposed models improve non-extractive machine reading comprehension (MRC) on the largest general domain multiple-choice dataset RACE. |
POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events (2022.emnlp-main)
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| Challenge: | Existing language models lag behind human performance in subtle ways in understanding complex situations, e.g., if the Argentine government yields to [IMF] pressure to rescind emergency legislation meant to protect ordinary families like the Brofmans. |
| Approach: | They propose to pre-identify a participant in a complex event and annotate their volitional engagement in causing the situation. |
| Outcome: | The proposed model can be used to infer the collective impact of salient events that make up a complex event, annotate volitional engagement of participants, and ground the outcome in state changes of the participants. |