Lin Zhao


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Fine-grained Artificial Neurons in Audio-transformers for Disentangling Neural Auditory Encoding
Mengyue Zhou | Xu Liu | David Liu | Zihao Wu | Zhengliang Liu | Lin Zhao | Dajiang Zhu | Lei Guo | Junwei Han | Tianming Liu | Xintao Hu
Findings of the Association for Computational Linguistics: ACL 2023

The Wav2Vec and its variants have achieved unprecedented success in computational auditory and speech processing. Meanwhile, neural encoding studies that integrate the superb representation capability of Wav2Vec and link those representations to brain activities have provided novel insights into a fundamental question of how auditory and speech processing unfold in the human brain. Without an explicit definition, most existing studies treat each transformer encoding layer in Wav2Vec as a single artificial neuron (AN). That is, the layer-level embeddings are used to predict neural responses. However, the comprehensive layer-level embedding aggregates multiple types of contextual attention captured by multi-head self-attention (MSA) modules. Thus, the layer-level ANs lack fine-granularity for neural encoding. To address this limitation, we define the elementary units, i.e., each hidden dimension, as neuron-level ANs in Wav2Vec2.0, quantify their temporal responses, and couple those ANs with their biological-neuron (BN) counterparts in the human brain. Our experimental results demonstrated that: 1) The proposed neuron-level ANs carry meaningful neurolinguistic information; 2) Those ANs anchor to their BN signatures; 3) The AN-BN anchoring patterns are interpretable from a neurolinguistic perspective. More importantly, our results suggest an intermediate stage in both the computational representation in Wav2Vec2.0 and the cortical representation in the brain. Our study validates the fine-grained ANs in Wav2Vec2.0, which may serve as a novel and general strategy to link transformer-based deep learning models to neural responses for probing the sensory processing in the brain.


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Structure-Infused Copy Mechanisms for Abstractive Summarization
Kaiqiang Song | Lin Zhao | Fei Liu
Proceedings of the 27th International Conference on Computational Linguistics

Seq2seq learning has produced promising results on summarization. However, in many cases, system summaries still struggle to keep the meaning of the original intact. They may miss out important words or relations that play critical roles in the syntactic structure of source sentences. In this paper, we present structure-infused copy mechanisms to facilitate copying important words and relations from the source sentence to summary sentence. The approach naturally combines source dependency structure with the copy mechanism of an abstractive sentence summarizer. Experimental results demonstrate the effectiveness of incorporating source-side syntactic information in the system, and our proposed approach compares favorably to state-of-the-art methods.

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Improving Slot Filling in Spoken Language Understanding with Joint Pointer and Attention
Lin Zhao | Zhe Feng
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We present a generative neural network model for slot filling based on a sequence-to-sequence (Seq2Seq) model together with a pointer network, in the situation where only sentence-level slot annotations are available in the spoken dialogue data. This model predicts slot values by jointly learning to copy a word which may be out-of-vocabulary (OOV) from an input utterance through a pointer network, or generate a word within the vocabulary through an attentional Seq2Seq model. Experimental results show the effectiveness of our slot filling model, especially at addressing the OOV problem. Additionally, we integrate the proposed model into a spoken language understanding system and achieve the state-of-the-art performance on the benchmark data.


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Using External Resources and Joint Learning for Bigram Weighting in ILP-Based Multi-Document Summarization
Chen Li | Yang Liu | Lin Zhao
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Improving Update Summarization via Supervised ILP and Sentence Reranking
Chen Li | Yang Liu | Lin Zhao
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees
Chen Li | Yang Liu | Fei Liu | Lin Zhao | Fuliang Weng
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)


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HIT: Web based Scoring Method for English Lexical Substitution
Shiqi Zhao | Lin Zhao | Yu Zhang | Ting Liu | Sheng Li
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)