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.

Dialogue State Tracking is central to multi-domain task-oriented dialogue systems, responsible for extracting information from user utterances. We present a novel hybrid architecture that augments GPT-2 with representations derived from Graph Attention Networks in such a way to allow causal, sequential prediction of slot values. The model architecture captures inter-slot relationships and dependencies across domains that otherwise can be lost in sequential prediction. We report improvements in state tracking performance in MultiWOZ 2.0 against a strong GPT-2 baseline and investigate a simplified sparse training scenario in which DST models are trained only on session-level annotations but evaluated at the turn level. We further report detailed analyses to demonstrate the effectiveness of graph models in DST by showing that the proposed graph modules capture inter-slot dependencies and improve the predictions of values that are common to multiple domains.

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(nˆ2) 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(nˆ2) 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.