Papers by Justin Chiu
Asking More Informative Questions for Grounded Retrieval (2024.findings-naacl)
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| Challenge: | Existing approaches to question generation for interactive retrieval have constrained answer spaces, limiting the amount of information a model can gain in a single turn. |
| Approach: | They propose a method that incorporates presupposition handling into question selection and belief updates. |
| Outcome: | The proposed method increases accuracy over the past state-of-the-art by 14% while resulting in 48% more efficient games in human evaluations. |
Hop, Union, Generate: Explainable Multi-hop Reasoning without Rationale Supervision (2023.emnlp-main)
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| Challenge: | Existing methods rely on supervision for both answers and rationales, but they have limited capacities in modeling interactions between sentences, let alone reasoning across multiple documents. |
| Approach: | They propose a principled, probabilistic approach for training explainable multi-hop question answering systems without rationale supervision. |
| Outcome: | The proposed method is more accurate at selecting rationales than previous methods while maintaining similar accuracy in predicting answers. |
Unsupervised Text Deidentification (2022.findings-emnlp)
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| Challenge: | Existing methods to deidentify textual data are supervised named entity recognition based on 18 identifiers like age, phone number, zip code . prior work ignores this nebulous 18th category, allowing for a supervised method to be used . |
| Approach: | They propose an unsupervised method that masks words that leak personally-identifying information. |
| Outcome: | The proposed method masks words that leak personally-identifying information . it eliminates many identifying aspects that would fall outside of the common named entity based approach . |
Reference-Centric Models for Grounded Collaborative Dialogue (2021.emnlp-main)
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| Challenge: | Using a structured referent grounding module, we can effectively ground and inform a partner's utterances to their own context. |
| Approach: | They propose a grounded neural dialogue model that works with people in a partially-observable reference game. |
| Outcome: | The proposed model outperforms state-of-the-art models on a spatial grounding dialogue task and achieves a 20% relative improvement in human evaluations. |
Retrieval-Enhanced Dual Encoder Training for Product Matching (2023.emnlp-industry)
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| Challenge: | Recent work has proposed a dual encoder for product matching due to its high performance and computation efficiency. |
| Approach: | They propose retrieval-enhanced dual encoder training to improve product matching . they use public and real-world product matching datasets to train the dual encoded model . |
| Outcome: | The proposed approach improves on a public and real-world product matching datasets. |
Predicting Text Preference Via Structured Comparative Reasoning (2024.acl-long)
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Jing Nathan Yan, Tianqi Liu, Justin Chiu, Jiaming Shen, Zhen Qin, Yue Yu, Charumathi Lakshmanan, Yair Kurzion, Alexander Rush, Jialu Liu, Michael Bendersky
| Challenge: | Existing approaches to comparative reasoning rely on pretraining or fine-tuning models at the cost of massive human annotation and computation. |
| Approach: | They propose a model that prompts LLMs to generate structured intermediate comparisons by proposing aspects for comparison, followed by generating textual comparisons under each aspect. |
| Outcome: | The proposed model significantly reduces hallucination and improves consistency across various NLP tasks. |
UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations (2024.naacl-long)
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Wenting Zhao, Justin Chiu, Jena Hwang, Faeze Brahman, Jack Hessel, Sanjiban Choudhury, Yejin Choi, Xiang Li, Alane Suhr
| Challenge: | Existing work evaluating commonsense reasoning focuses on making inferences about common, everyday situations. |
| Approach: | They propose to use an English language corpus to investigate commonsense reasoning . they characterize performance differences between human explainers and best-performing large language models . |
| Outcome: | The proposed method reduces the loss rate of human-written explanations on commonsense reasoning compared with the vanilla supervised fine-tuning approach . |
Scaling Hidden Markov Language Models (2020.emnlp-main)
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| Challenge: | Hidden Markov models are a fundamental tool for sequence modeling that separates the hidden state from the emission structure. |
| Approach: | They propose methods for scaling hidden Markov models to massive state spaces while maintaining efficient exact inference and effective regularization. |
| Outcome: | The proposed methods are much more accurate than previous HMMs and n-gram-based methods, making progress towards the performance of state-of-the-art NN models. |
Cross-Encoder Data Annotation for Bi-Encoder Based Product Matching (2022.emnlp-industry)
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| Challenge: | Existing approaches to match seller listed items to appropriate product are computationally heavy and require computational resources. |
| Approach: | They propose a technique to annotate or refine human annotated training data for bi-encoder models using a cross-encoding model. |
| Outcome: | The proposed approach improves 4% absolute accuracy when no training data is available and 2% when annotated training data exists. |
Abductive Commonsense Reasoning Exploiting Mutually Exclusive Explanations (2023.acl-long)
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| Challenge: | Existing approaches for abductive reasoning in natural language processing rely on manual supervision. |
| Approach: | They propose an approach for abductive commonsense reasoning that exploits the fact that only a subset of explanations is correct for a given context. |
| Outcome: | The proposed approach outperforms or is comparable to knowledge-augmented zero-shot methods on a diverse set of abductive reasoning datasets. |
Symbolic Planning and Code Generation for Grounded Dialogue (2023.emnlp-main)
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| Challenge: | Large language models excel at processing and generating text and code, but lack a grounded task-oriented dialogue system that can handle grounding. |
| Approach: | They propose a modular and interpretable grounded dialogue system that integrates a reader and planner to convert partner utterances into executable code and a symbolic planner to determine the next appropriate response. |
| Outcome: | The proposed system outperforms the existing state-of-the-art on a one-common dialogue task and improves task success in human evaluations from 56% to 69% in the most challenging setting. |