Papers by Christopher Hidey
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking (2020.acl-main)
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Christopher Hidey, Tuhin Chakrabarty, Tariq Alhindi, Siddharth Varia, Kriste Krstovski, Mona Diab, Smaranda Muresan
| Challenge: | Fact Extraction and Verification datasets provide a resource for end-to-end fact-checking, requiring retrieval of evidence from Wikipedia to validate a veracity prediction. |
| Approach: | They propose a system that is resilient to attacks by multiple propositions, temporal reasoning, ambiguity and lexical variation and a sequence of evidence sentences and veracity relation predictions. |
| Outcome: | The proposed system is resilient to three realistic “attacks” and obtains state-of-the-art results due to improved evidence retrieval. |
ENTRUST: Argument Reframing with Language Models and Entailment (2021.naacl-main)
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| Challenge: | Public opinion has been shown to be significantly influenced by framing effects. |
| Approach: | They propose a method for reframing arguments that combines controllable text generation with a post-decoding entailment component to achieve the same denotation. |
| Outcome: | The proposed method is effective compared to baselines along the dimensions of fluency, meaning, and trustworthiness/reduction of fear. |
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions (D19-1)
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| Challenge: | Argument mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text. |
| Approach: | They propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macro-level models of argumentation. |
| Outcome: | The proposed model improves on existing models using pointer networks and a pre-trained language model. |
IMHO Fine-Tuning Improves Claim Detection (N19-1)
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| Challenge: | Empirical results show that using this approach improves the state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points in claim detection. |
| Approach: | They propose to fine-tune a language model using a Reddit corpus of opinionated claims and use the internet acronyms IMO/IMHO to identify claims. |
| Outcome: | The proposed approach improves state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points. |
Compute-Efficient Churn Reduction for Conversational Agents (2023.emnlp-industry)
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| Challenge: | churn occurs when retraining models yields different predictions despite using the same data and hyper-parameters. |
| Approach: | They propose a method that pairs semantic parses based on their “function call signature” and encourages similarity through an additional loss based upon Jensen-Shannon Divergence. |
| Outcome: | The proposed method improves in academic, noisy, and industry settings. |
Fixed That for You: Generating Contrastive Claims with Semantic Edits (N19-1)
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| Challenge: | Understanding contrastive opinions is a key component of argument generation. |
| Approach: | They create a corpus of Reddit comment pairs and train neural models to edit the original claim and produce a new claim with a different view. |
| Outcome: | The proposed model improves on a sequence-to-sequence baseline and compared to a human evaluation for fluency, coherence, and contrast. |
DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue (2023.acl-long)
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| Challenge: | Existing studies show that multilingual models are less robust for semantic parsing compared to other tasks. |
| Approach: | They propose a constrained optimization technique to optimize multilingual parsing systems for multilingual use. |
| Outcome: | The proposed technique outperforms XLM-R and mT5-Large on three benchmarks and significantly outperformed other models. |