Papers by Jinghui Qin
HutCRS: Hierarchical User-Interest Tracking for Conversational Recommender System (2023.emnlp-main)
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| Challenge: | Existing CRSs assume that users like all attributes of the target item and dislike those unrelated to it, which can introduce bias in attribute-level feedback and impede the system’s ability to accurately identify the target items. |
| Approach: | They propose a framework that allows users to explicitly acquire user preferences through natural language conversations by providing explicit answers (yes/no) for each attribute they require. |
| Outcome: | The proposed framework portrays the conversation as a hierarchical interest tree that consists of two stages. |
Unbiased Math Word Problems Benchmark for Mitigating Solving Bias (2022.findings-naacl)
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| Challenge: | Existing solvers with data bias and learning bias only learn shallow heuristics rather than deep semantics for understanding problems. |
| Approach: | They propose a MWP dataset named UnbiasedMWP which is constructed by varying the grounded expressions in collected data and annotating them manually. |
| Outcome: | The proposed dataset has significantly fewer biases than its original data and other datasets, posing a promising benchmark for fairly evaluating the solvers’ reasoning skills rather than matching nearest neighbors. |
GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning (2021.findings-acl)
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| Challenge: | Existing methods to solve geometric problems are dependent on handcraft rules and limited on small-scale datasets. |
| Approach: | They propose a Geometric Question Answering dataset with 5,010 geometric problems with corresponding annotated programs to illustrate the solving process. |
| Outcome: | The proposed method is significantly lower than human performance on the proposed dataset than on a publicly available dataset. |
CEM: Machine-Human Chatting Handoff via Causal-Enhance Module (2022.emnlp-main)
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| Challenge: | Existing methods to predict chatbot failure ignore causal variables, resulting in cost increasement and prediction bias. |
| Approach: | They propose a machine-human chatting handoff module that predicts chatbot failure . they use user state and labor cost to correct the prediction bias . |
| Outcome: | The proposed method improves the performance of existing methods without any elaborate model crafting. |
HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation (2024.acl-long)
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| Challenge: | Existing methods to study the Matthew effect in Recommender Systems (RSs) however, it is amplified when the user interacts with the system over time. |
| Approach: | They propose a paradigm to alleviate the Matthew effect in conversational recommendation by learning multi-aspect preferences. |
| Outcome: | The proposed paradigm achieves state-of-the-art performance and superior of alleviating Matthew effect in conversational recommendation tasks. |
UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression (2022.emnlp-main)
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| Challenge: | Existing work on geometry problem solving treats calculation and proving as two specific tasks hindering a deep model to unify reasoning ability on multiple math tasks. |
| Approach: | They propose a large-scale Unified Geometry problem benchmark to unify geometry on multiple math tasks. |
| Outcome: | The proposed framework outperforms the existing model with 5.6% and 3.2% accuracies on calculation and proving problems. |
AssoCiAm: A Benchmark for Evaluating Association Thinking while Circumventing Ambiguity (2025.emnlp-main)
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| Challenge: | Recent advances in multimodal large language models (MLLMs) have garnered significant attention, offering a promising pathway toward artificial general intelligence (AGI). |
| Approach: | They propose a benchmark to evaluate associative ability while circumventing the inherent ambiguity in association tasks by decomposing ambiguities into two types and propose 'assoCiAm' they conduct extensive experiments on MLLMs, revealing a strong positive correlation between cognition and association. |
| Outcome: | The proposed method shows that ambiguity in association evaluations makes MLLMs more random-like and the model's behavior more random. |
Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks (2021.acl-long)
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| Challenge: | Existing solutions for math word problems lack explicit integration of math symbolic constraints, leading to unexplainable and unreasonable predictions. |
| Approach: | They propose a novel mathematical model that explicitly incorporates symbolic constraints by auxiliary tasks to enforce different symbolic reasoning. |
| Outcome: | The proposed solver incorporates symbolic constraints by auxiliary tasks to enforce different symbolic reasoning. |
Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems (2020.emnlp-main)
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| Challenge: | Existing models focus on one-unknown linear MWPs. |
| Approach: | They propose a universal expression tree-structured solver that integrates multiple expression trees underlying a MWP into a single expression tree. |
| Outcome: | The proposed method outperforms state-of-the-art models on a MWPs dataset and generates a universal expression tree explicitly by deciding which symbol to generate . |
GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems (2020.emnlp-main)
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| Challenge: | Existing evaluation metrics only consider surface features or utterance-level semantics, without explicitly considering the fine-grained topic transition dynamics of dialogue flows. |
| Approach: | They propose a graph-enhanced evaluation metric GRADE to evaluate dialogue coherence . GRADE incorporates utterance-level contextualized representations and fine-grained topic-level graph representations to improve communication logic. |
| Outcome: | The proposed evaluation metric outperforms state-of-the-art metrics on measuring diverse dialogue models in terms of Pearson and Spearman correlations with human judgments. |
LogicSolver: Towards Interpretable Math Word Problem Solving with Logical Prompt-enhanced Learning (2022.findings-emnlp)
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| Challenge: | Recent advances in MWP solving are uninterpretable due to shallow heuristics . a new approach to solve automatic word problem solvers requires a solver to predict expression tree and corresponding linguistic logic formulas simultaneously. |
| Approach: | They propose to annotate interpretable logical formulas based on algebraic knowledge as the grounded linguistic logic of each solution equation. |
| Outcome: | The proposed approach improves interpretability of a MWP solver by using logical prompts and interpretation generation. |