Structured Self-Attention Weights Encode Semantics in Sentiment Analysis

Neural attention, especially the self-attention made popular by the Transformer, has become the workhorse of state-of-the-art natural language processing (NLP) models. Very recent work suggests that the self-attention in the Transformer encodes syntactic information; Here, we show that self-attention scores encode semantics by considering sentiment analysis tasks. In contrast to gradient-based feature attribution methods, we propose a simple and effective Layer-wise Attention Tracing (LAT) method to analyze structured attention weights. We apply our method to Transformer models trained on two tasks that have surface dissimilarities, but share common semantics---sentiment analysis of movie reviews and time-series valence prediction in life story narratives. Across both tasks, words with high aggregated attention weights were rich in emotional semantics, as quantitatively validated by an emotion lexicon labeled by human annotators. Our results show that structured attention weights encode rich semantics in sentiment analysis, and match human interpretations of semantics.

Learning to Caption Images with Two-Stream Attention and Sentence Auto-Encoder

Automatically generating natural language descriptions from an image is a challenging problem in artificial intelligence that requires a good understanding of the correlations between visual and textual cues. To bridge these two modalities, state-of-the-art methods commonly use a dynamic interface between image and text, called attention, that learns to identify related image parts to estimate the next word conditioned on the previous steps. While this mechanism is effective, it fails to find the right associations between visual and textual cues when they are noisy. In this paper we propose two novel approaches to address this issue - (i) a two-stream attention mechanism that can automatically discover latent categories and relate them to image regions based on the previously generated words, (ii) a regularization technique that encapsulates the syntactic and semantic structure of captions and improves the optimization of the image captioning model. Our qualitative and quantitative results demonstrate remarkable improvements on the MSCOCO dataset setting and lead to new state-of-the-art performances for image captioning.

A Multimodal LSTM for Predicting Listener Empathic Responses Over Time

People naturally understand the emotions of-and often also empathize with-those around them. In this paper, we predict the emotional valence of an empathic listener over time as they listen to a speaker narrating a life story. We use the dataset provided by the OMG-Empathy Prediction Challenge, a workshop held in conjunction with IEEE FG 2019. We present a multimodal LSTM model with feature-level fusion and local attention that predicts empathic responses from audio, text, and visual features. Our best-performing model, which used only the audio and text features, achieved a concordance correlation coefficient (CCC) of 0.29 and 0.32 on the Validation set for the Generalized and Personalized track respectively, and achieved a CCC of 0.14 and 0.14 on the held-out Test set. We discuss the difficulties faced and the lessons learnt tackling this challenge.