Hao Tang


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AMR Parsing with Latent Structural Information
Qiji Zhou | Yue Zhang | Donghong Ji | Hao Tang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Abstract Meaning Representations (AMRs) capture sentence-level semantics structural representations to broad-coverage natural sentences. We investigate parsing AMR with explicit dependency structures and interpretable latent structures. We generate the latent soft structure without additional annotations, and fuse both dependency and latent structure via an extended graph neural networks. The fused structural information helps our experiments results to achieve the best reported results on both AMR 2.0 (77.5% Smatch F1 on LDC2017T10) and AMR 1.0 ((71.8% Smatch F1 on LDC2014T12).

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Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification
Hao Tang | Donghong Ji | Chenliang Li | Qiji Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Aspect-based sentiment classification is a popular task aimed at identifying the corresponding emotion of a specific aspect. One sentence may contain various sentiments for different aspects. Many sophisticated methods such as attention mechanism and Convolutional Neural Networks (CNN) have been widely employed for handling this challenge. Recently, semantic dependency tree implemented by Graph Convolutional Networks (GCN) is introduced to describe the inner connection between aspects and the associated emotion words. But the improvement is limited due to the noise and instability of dependency trees. To this end, we propose a dependency graph enhanced dual-transformer network (named DGEDT) by jointly considering the flat representations learnt from Transformer and graph-based representations learnt from the corresponding dependency graph in an iterative interaction manner. Specifically, a dual-transformer structure is devised in DGEDT to support mutual reinforcement between the flat representation learning and graph-based representation learning. The idea is to allow the dependency graph to guide the representation learning of the transformer encoder and vice versa. The results on five datasets demonstrate that the proposed DGEDT outperforms all state-of-the-art alternatives with a large margin.


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Relating Simple Sentence Representations in Deep Neural Networks and the Brain
Sharmistha Jat | Hao Tang | Partha Talukdar | Tom Mitchell
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

What is the relationship between sentence representations learned by deep recurrent models against those encoded by the brain? Is there any correspondence between hidden layers of these recurrent models and brain regions when processing sentences? Can these deep models be used to synthesize brain data which can then be utilized in other extrinsic tasks? We investigate these questions using sentences with simple syntax and semantics (e.g., The bone was eaten by the dog.). We consider multiple neural network architectures, including recently proposed ELMo and BERT. We use magnetoencephalography (MEG) brain recording data collected from human subjects when they were reading these simple sentences. Overall, we find that BERT’s activations correlate the best with MEG brain data. We also find that the deep network representation can be used to generate brain data from new sentences to augment existing brain data. To the best of our knowledge, this is the first work showing that the MEG brain recording when reading a word in a sentence can be used to distinguish earlier words in the sentence. Our exploration is also the first to use deep neural network representations to generate synthetic brain data and to show that it helps in improving subsequent stimuli decoding task accuracy.


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Discriminative Pronunciation Modeling: A Large-Margin, Feature-Rich Approach
Hao Tang | Joseph Keshet | Karen Livescu
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Spherical Discriminant Analysis in Semi-supervised Speaker Clustering
Hao Tang | Stephen Chu | Thomas Huang
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers