Papers by Ashutosh Baheti
Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts (2021.emnlp-main)
Copied to clipboard
| Challenge: | despite progress toward data-driven conversational agents, dialogue models still suffer from issues surrounding safety and offensive language. |
| Approach: | They analyze reddit threads and reddits to determine the stance of offensive dialogue models . they find 42% of human responses agree with toxic comments, compared to 13% with safe comments . |
| Outcome: | The proposed model produces 29% fewer offensive replies than the baseline model. |
Extracting a Knowledge Base of COVID-19 Events from Social Media (2022.coling-1)
Copied to clipboard
| Challenge: | a flood of COVID-19 related information has appeared on social media since December 2019 . this includes reports on public figures who have tested positive/negative for the virus . |
| Approach: | They construct a corpus of 10,000 tweets with annotated public reports of five COVID-19 events, using slot-filling questions to fill in slots. |
| Outcome: | The proposed method can be quickly applied to develop knowledge bases for new domains in response to emerging crises, including natural disasters or future disease outbreaks. |
NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation (2023.findings-emnlp)
Copied to clipboard
Peter West, Ronan Bras, Taylor Sorensen, Bill Lin, Liwei Jiang, Ximing Lu, Khyathi Chandu, Jack Hessel, Ashutosh Baheti, Chandra Bhagavatula, Yejin Choi
| Challenge: | a new commonsense knowledge model, NovaCOMET, combines knowledge and general task models. |
| Approach: | They propose an open commonsense knowledge model that combines knowledge and general task models. |
| Outcome: | The proposed model matches or exceeds existing knowledge models on commonsense reasoning tasks. |
Fluent Response Generation for Conversational Question Answering (2020.acl-main)
Copied to clipboard
| Challenge: | Question answering (QA) is an important aspect of open-domain conversational agents, garnering specific research focus in the conversational QA subtask. |
| Approach: | They propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness. |
| Outcome: | The proposed model outperforms baseline CoQA and QuAC models in generating conversational responses. |
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints (D18-1)
Copied to clipboard
| Challenge: | Neural conversation models tend to generate safe, generic responses for most inputs . this is due to the limitations of likelihood-based decoding objectives in generation tasks with diverse outputs, such as conversation. |
| Approach: | They propose a distributional constraint approach that incorporates side information into the generated responses. |
| Outcome: | The proposed approach generates responses that are less generic without sacrificing plausibility. |
Stanceosaurus: Classifying Stance Towards Multicultural Misinformation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing corpora focus on misinformation spreading within western countries. |
| Approach: | They present a new corpus of tweets annotated with stance towards 250 misinformation claims. |
| Outcome: | The proposed method achieves 53.1 F1 on Hindi and 50.4 F1 in Arabic without any target-language fine-tuning. |