Papers by Linzi Xing
Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning (2021.acl-short)
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| Challenge: | In news articles the lead bias dominates the learning signals for neural extractive summarizations, severely limiting their performance on data with different or even no bias. |
| Approach: | They propose a method to demote the lead bias in news and make the model focus more on the content semantics. |
| Outcome: | The proposed method can demote the model’s learned lead bias and improve its generality on out-of-distribution data with little to no performance loss on in-difference data. |
Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition (2020.lrec-1)
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| Challenge: | Existing work on document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes. |
| Approach: | They assemble and publish a multilingual Twitter corpus for the task of hate speech detection using inferred author demographic factors. |
| Outcome: | The results show that the classifiers learn human biases and can be discriminatory towards certain demographic groups. |
Improving Context Modeling in Neural Topic Segmentation (2020.aacl-main)
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| Challenge: | Recent work favors highly effective neural supervised approaches for topic segmentation but current neural solutions are limited in how they model context. |
| Approach: | They propose to enhance a hierarchical attention biLSTM network-based topic segmenter to better model context by adding a coherence-related auxiliary task and restricted self-attention. |
| Outcome: | The proposed model outperforms SOTA approaches on three datasets and on four real-world benchmarks. |
Diversity-Aware Coherence Loss for Improving Neural Topic Models (2023.acl-short)
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| Challenge: | Experimental results show that our method significantly improves the performance of neural topic models without requiring any pretraining or additional parameters. |
| Approach: | They propose a variational autoencoder framework that minimizes the posterior and prior divergence and a diversity-aware coherence loss that encourages the model to learn corpus-level coherency scores while maintaining high diversity between topics. |
| Outcome: | The proposed approach significantly improves the performance of neural topic models without pretraining or additional parameters. |
Towards Human-aligned Evaluation for Linear Programming Word Problems (2024.lrec-main)
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Linzi Xing, Xinglu Wang, Yuxi Feng, Zhenan Fan, Jing Xiong, Zhijiang Guo, Xiaojin Fu, Rindra Ramamonjison, Mahdi Mostajabdaveh, Xiongwei Han, Zirui Zhou, Yong Zhang
| Challenge: | Existing evaluation methodologies for MWPs diverge from human judgment and face challenges in recognizing mathematically equivalent answers. |
| Approach: | They propose an evaluation metric rooted in graph edit distance that features benefits such as permutation invariance and more accurate program equivalence identification. |
| Outcome: | The proposed evaluation metric features benefits such as permutation invariance and more accurate program equivalence identification. |
LaTeX2Solver: a Hierarchical Semantic Parsing of LaTeX Document into Code for an Assistive Optimization Modeling Application (2023.acl-demo)
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Rindra Ramamonjison, Timothy Yu, Linzi Xing, Mahdi Mostajabdaveh, Xiaorui Li, Xiaojin Fu, Xiongwei Han, Yuanzhe Chen, Ren Li, Kun Mao, Yong Zhang
| Challenge: | Existing systems that translate optimization formulas manually are cumbersome and time-consuming. |
| Approach: | They propose a system that converts optimization formulas from TeX document to solver language. |
| Outcome: | The proposed system helps operations research practitioners convert optimization formulations into solver modeling languages. |
Evaluating Topic Quality with Posterior Variability (D19-1)
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| Challenge: | Probabilistic topic models such as latent Dirichlet allocation (LDA) are widely used for NLP tasks which require the extraction of latent themes. |
| Approach: | They propose to measure topic quality using the variability of posterior distributions of probabilistic topic models. |
| Outcome: | The proposed metric achieves state-of-the-art correlations with human judgments of topic quality in experiments on three corpora. |
Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation (2022.emnlp-main)
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| Challenge: | Existing studies have investigated the multi-head self-attention mechanism of transformers. |
| Approach: | They propose to use a human-in-the-loop pipeline to discover task-specific attention patterns and inject them into transformer models to improve their accuracy. |
| Outcome: | The proposed methods improve the performance of transformer models by incorporating predefined patterns into their attention matrices. |