publications

2021

1. Extracting and Inferring Personal Attributes from Dialogue

   Wang, Zhilin, Zhou, Xuhui, Koncel-Kedziorski, Rik, Marin, Alex, and Xia, Fei

   In Conference Review 2021

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   Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes. In this work, we introduce the tasks of extracting and inferring personal attributes from human-human dialogue. We first demonstrate the benefit of incorporating personal attributes in a social chit-chat dialogue model and task-oriented dialogue setting. Thus motivated, we propose the tasks of personal attribute extraction and inference, and then analyze the linguistic demands of these tasks. To meet these challenges, we introduce a simple and extensible model that combines an autoregressive language model utilizing constrained attribute generation with a discriminative reranker. Our model outperforms strong baselines on extracting personal attributes as well as inferring personal attributes that are not contained verbatim in utterances and instead requires commonsense reasoning and lexical inferences, which occur frequently in everyday conversation.

2. Plot Twist: Uncovering Surprising Event Boundaries in Narratives

   Wang, Zhilin, Jafarpour, Anna, and Sap, Maarten

   In Conference Review 2021

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   When reading stories, people can naturally identify sentences in which a new event starts, i.e., event boundaries, using their knowledge of how events typically unfold, but a computational model to detect event boundaries is not yet available. In this study, we characterize and detect sentences with expected or surprising event boundaries in an annotated corpus of short diary-like stories. We train a detection model that combines commonsense knowledge and narrative flow features with a RoBERTa classifier. Our results show that, while commonsense and narrative features can help improve performance overall, detecting event boundaries that are more subjective remains challenging for our model. Upon inspection of the model parameters, we find that sentences marking surprising event boundaries are less likely to be causally related to the preceding sentence, but are more likely to express emotional reactions of story characters, compared to sentences with no event boundary. Additionally, our model performs well in the closely related task of detecting stories with endings that do not follow commonsense from a different corpus. Our results show promise of using models with commonsense knowledge and narrative flow features to shed light on detecting event boundaries in narratives.

3. Learning Similarity between Movie Characters and Its Potential Implications on Understanding Human Experiences

   Wang, Zhilin, Lin, Weizhe, and Wu, Xiaodong

   In Proceedings of the Third Workshop on Narrative Understanding @ NAACL 2021

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   While many different aspects of human experiences have been studied by the NLP community, none has captured its full richness. We propose a new task to capture this richness based on an unlikely setting: movie characters. We sought to capture theme-level similarities between movie characters that were community-curated into 20,000 themes. By introducing a two-step approach that balances performance and efficiency, we managed to achieve 9-27% improvement over recent paragraph-embedding based methods. Finally, we demonstrate how the thematic information learnt from movie characters can potentially be used to understand themes in the experience of people, as indicated on Reddit posts.

2019

1. No, you’re not alone: A better way to find people with similar experiences on Reddit

   Wang, Zhilin, Rastorgueva, Elena, Lin, Weizhe, and Wu, Xiaodong

   In Proceedings of the 5th Workshop on Noisy User-generated Text @ EMNLP 2019

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   We present a probabilistic clustering algorithm that can help Reddit users to find posts that discuss experiences similar to their own. This model is built upon the BERT Next Sentence Prediction model and reduces the time complexity for clustering all posts in a corpus from O(n\^2) to O(n) with respect to the number of posts. We demonstrate that such probabilistic clustering can yield a performance better than baseline clustering methods based on Latent Dirichlet Allocation (Blei et al., 2003) and Word2Vec (Mikolov et al., 2013). Furthermore, there is a high degree of coherence between our probabilistic clustering and the exhaustive comparison O(n\^2) algorithm in which the similarity between every pair of posts is found. This makes the use of the BERT Next Sentence Prediction model more practical for unsupervised clustering tasks due to the high runtime overhead of each BERT computation.