Xinyi Wu


2021

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Explaining Relationships Between Scientific Documents
Kelvin Luu | Xinyi Wu | Rik Koncel-Kedziorski | Kyle Lo | Isabel Cachola | Noah A. Smith
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We address the task of explaining relationships between two scientific documents using natural language text. This task requires modeling the complex content of long technical documents, deducing a relationship between these documents, and expressing the details of that relationship in text. In addition to the theoretical interest of this task, successful solutions can help improve researcher efficiency in search and review. In this paper we establish a dataset of 622K examples from 154K documents. We pretrain a large language model to serve as the foundation for autoregressive approaches to the task. We explore the impact of taking different views on the two documents, including the use of dense representations extracted with scientific IE systems. We provide extensive automatic and human evaluations which show the promise of such models, but make clear challenges for future work.

2020

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Linguistically-Informed Transformations (LIT): A Method for Automatically Generating Contrast Sets
Chuanrong Li | Lin Shengshuo | Zeyu Liu | Xinyi Wu | Xuhui Zhou | Shane Steinert-Threlkeld
Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

Although large-scale pretrained language models, such as BERT and RoBERTa, have achieved superhuman performance on in-distribution test sets, their performance suffers on out-of-distribution test sets (e.g., on contrast sets). Building contrast sets often requires human-expert annotation, which is expensive and hard to create on a large scale. In this work, we propose a Linguistically-Informed Transformation (LIT) method to automatically generate contrast sets, which enables practitioners to explore linguistic phenomena of interests as well as compose different phenomena. Experimenting with our method on SNLI and MNLI shows that current pretrained language models, although being claimed to contain sufficient linguistic knowledge, struggle on our automatically generated contrast sets. Furthermore, we improve models’ performance on the contrast sets by applying LIT to augment the training data, without affecting performance on the original data.

2019

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What Makes a Good Counselor? Learning to Distinguish between High-quality and Low-quality Counseling Conversations
Verónica Pérez-Rosas | Xinyi Wu | Kenneth Resnicow | Rada Mihalcea
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The quality of a counseling intervention relies highly on the active collaboration between clients and counselors. In this paper, we explore several linguistic aspects of the collaboration process occurring during counseling conversations. Specifically, we address the differences between high-quality and low-quality counseling. Our approach examines participants’ turn-by-turn interaction, their linguistic alignment, the sentiment expressed by speakers during the conversation, as well as the different topics being discussed. Our results suggest important language differences in low- and high-quality counseling, which we further use to derive linguistic features able to capture the differences between the two groups. These features are then used to build automatic classifiers that can predict counseling quality with accuracies of up to 88%.