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We describe a span-level supervised attention loss that improves compositional generalization in semantic parsers. Our approach builds on existing losses that encourage attention maps in neural sequence-to-sequence models to imitate the output of classical word alignment algorithms. Where past work has used word-level alignments, we focus on spans; borrowing ideas from phrase-based machine translation, we align subtrees in semantic parses to spans of input sentences, and encourage neural attention mechanisms to mimic these alignments. This method improves the performance of transformers, RNNs, and structured decoders on three benchmarks of compositional generalization.

NAACL 2021

Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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technical paper

Current sequence-to-sequence models are trained to minimize cross-entropy and use softmax to compute the locally normalized probabilities over target sequences.

Smoothing and Shrinking the Sparse Seq2Seq Search Space

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Can Latent Alignments Improve Autoregressive Machine Translation?

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Continual Learning for Text Classification with Information Disentanglement Based Regularization

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Cross-Lingual Word Embedding Refinement by $\ell_{1}$ Norm Optimisation

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Semantic Frame Forecast

The capability to automatically detect human stress can benefit artificial intelligent agents involved in affective computing and human-computer interaction. Stress and emotion are both human affective states, and stress has proven to have important implications on the regulation and expression of emotion. Although a series of methods have been established for multimodal stress detection, limited steps have been taken to explore the underlying inter-dependence between stress and emotion. In this work, we investigate the value of emotion recognition as an auxiliary task to improve stress detection. We propose MUSER -- a transformer-based model architecture and a novel multi-task learning algorithm with speed-based dynamic sampling strategy. Evaluation on the Multimodal Stressed Emotion (MuSE) dataset shows that our model is effective for stress detection with both internal and external auxiliary tasks, and achieves state-of-the-art results.

MUSER: MUltimodal Stress detection using Emotion Recognition as an Auxiliary Task

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Semantic parsing aims at translating natural language (NL) utterances onto machine- interpretable programs, which can be executed against a real-world environment. The expensive annotation of utterance-program pairs has long been acknowledged as a major bottleneck for the deployment of contemporary neural models to real-life applications. In this work, we focus on the task of semi-supervised learning where a limited amount of annotated data is available together with many unlabeled NL utterances. Based on the observation that programs which correspond to NL utterances must be always executable, we propose to encourage a parser to generate executable programs for unlabeled utterances. Due to the large search space of executable programs, conventional methods that use approximations based on beam-search such as self-training and top-k marginal likelihood training, do not perform as well. Instead, we view the problem of learning from executions from the perspective of posterior regularization and propose a set of new training objectives. Experimental results on OVERNIGHT and GEOQUERY show that our new objectives outperform conventional methods, bridging the gap between semi-supervised and supervised learning.

Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention

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