Shuguang Chen


2022

pdf bib
A Simple Approach to Jointly Rank Passages and Select Relevant Sentences in the OBQA Context
Man Luo | Shuguang Chen | Chitta Baral
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop

In the open book question answering (OBQA) task, selecting the relevant passages and sentences from distracting information is crucial to reason the answer to a question. HotpotQA dataset is designed to teach and evaluate systems to do both passage ranking and sentence selection. Many existing frameworks use separate models to select relevant passages and sentences respectively. Such systems not only have high complexity in terms of the parameters of models but also fail to take the advantage of training these two tasks together since one task can be beneficial for the other one. In this work, we present a simple yet effective framework to address these limitations by jointly ranking passages and selecting sentences. Furthermore, we propose consistency and similarity constraints to promote the correlation and interaction between passage ranking and sentence selection.The experiments demonstrate that our framework can achieve competitive results with previous systems and outperform the baseline by 28% in terms of exact matching of relevant sentences on the HotpotQA dataset.

2021

pdf bib
Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching
Thamar Solorio | Shuguang Chen | Alan W. Black | Mona Diab | Sunayana Sitaram | Victor Soto | Emre Yilmaz | Anirudh Srinivasan
Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching

pdf bib
Mitigating Temporal-Drift: A Simple Approach to Keep NER Models Crisp
Shuguang Chen | Leonardo Neves | Thamar Solorio
Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media

Performance of neural models for named entity recognition degrades over time, becoming stale. This degradation is due to temporal drift, the change in our target variables’ statistical properties over time. This issue is especially problematic for social media data, where topics change rapidly. In order to mitigate the problem, data annotation and retraining of models is common. Despite its usefulness, this process is expensive and time-consuming, which motivates new research on efficient model updating. In this paper, we propose an intuitive approach to measure the potential trendiness of tweets and use this metric to select the most informative instances to use for training. We conduct experiments on three state-of-the-art models on the Temporal Twitter Dataset. Our approach shows larger increases in prediction accuracy with less training data than the alternatives, making it an attractive, practical solution.

pdf bib
Can images help recognize entities? A study of the role of images for Multimodal NER
Shuguang Chen | Gustavo Aguilar | Leonardo Neves | Thamar Solorio
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)

Multimodal named entity recognition (MNER) requires to bridge the gap between language understanding and visual context. While many multimodal neural techniques have been proposed to incorporate images into the MNER task, the model’s ability to leverage multimodal interactions remains poorly understood. In this work, we conduct in-depth analyses of existing multimodal fusion techniques from different perspectives and describe the scenarios where adding information from the image does not always boost performance. We also study the use of captions as a way to enrich the context for MNER. Experiments on three datasets from popular social platforms expose the bottleneck of existing multimodal models and the situations where using captions is beneficial.

pdf bib
Data Augmentation for Cross-Domain Named Entity Recognition
Shuguang Chen | Gustavo Aguilar | Leonardo Neves | Thamar Solorio
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-domain data in low-resource scenarios where annotated data is quite limited. In this work, we take this research direction to the opposite and study cross-domain data augmentation for the NER task. We investigate the possibility of leveraging data from high-resource domains by projecting it into the low-resource domains. Specifically, we propose a novel neural architecture to transform the data representation from a high-resource to a low-resource domain by learning the patterns (e.g. style, noise, abbreviations, etc.) in the text that differentiate them and a shared feature space where both domains are aligned. We experiment with diverse datasets and show that transforming the data to the low-resource domain representation achieves significant improvements over only using data from high-resource domains.