Kaisheng Yao


2024

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Zero-Shot End-to-End Spoken Language Understanding via Cross-Modal Selective Self-Training
Jianfeng He | Julian Salazar | Kaisheng Yao | Haoqi Li | Jason Cai
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

End-to-end (E2E) spoken language understanding (SLU) is constrained by the cost of collecting speech-semantics pairs, especially when label domains change. Hence, we explore zero-shot E2E SLU, which learns E2E SLU without speech-semantics pairs, instead using only speech-text and text-semantics pairs. Previous work achieved zero-shot by pseudolabeling all speech-text transcripts with a natural language understanding (NLU) model learned on text-semantics corpora. However, this method requires the domains of speech-text and text-semantics to match, which often mismatch due to separate collections. Furthermore, using the entire collected speech-text corpus from any domains leads to imbalance and noise issues. To address these, we propose cross-modal selective self-training (CMSST). CMSST tackles imbalance by clustering in a joint space of the three modalities (speech, text, and semantics) and handles label noise with a selection network. We also introduce two benchmarks for zero-shot E2E SLU, covering matched and found speech (mismatched) settings. Experiments show that CMSST improves performance in both two settings, with significantly reduced sample sizes and training time. Our code and data are released in https://github.com/amazon-science/zero-shot-E2E-slu.

2023

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Enhancing Abstractiveness of Summarization Models through Calibrated Distillation
Hwanjun Song | Igor Shalyminov | Hang Su | Siffi Singh | Kaisheng Yao | Saab Mansour
Findings of the Association for Computational Linguistics: EMNLP 2023

In this paper, we propose a novel approach named DisCal to enhance the level of abstractiveness (measured by n-gram overlap) without sacrificing the informativeness (measured by ROUGE) of generated summaries. DisCal exposes diverse pseudo summaries with two supervision to the student model. Firstly, the best pseudo summary is identified in terms of abstractiveness and informativeness and used for sequence-level distillation. Secondly, their ranks are used to ensure the student model to assign higher prediction scores to summaries with higher ranks. Our experiments show that DisCal outperforms prior methods in abstractive summarization distillation, producing highly abstractive and informative summaries.

2021

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Rewriter-Evaluator Architecture for Neural Machine Translation
Yangming Li | Kaisheng Yao
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

A few approaches have been developed to improve neural machine translation (NMT) models with multiple passes of decoding. However, their performance gains are limited because of lacking proper policies to terminate the multi-pass process. To address this issue, we introduce a novel architecture of Rewriter-Evaluator. Translating a source sentence involves multiple rewriting passes. In every pass, a rewriter generates a new translation to improve the past translation. Termination of this multi-pass process is determined by a score of translation quality estimated by an evaluator. We also propose prioritized gradient descent (PGD) to jointly and efficiently train the rewriter and the evaluator. Extensive experiments on three machine translation tasks show that our architecture notably improves the performances of NMT models and significantly outperforms prior methods. An oracle experiment reveals that it can largely reduce performance gaps to the oracle policy. Experiments confirm that the evaluator trained with PGD is more accurate than prior methods in determining proper numbers of rewriting.

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Neural Sequence Segmentation as Determining the Leftmost Segments
Yangming Li | Lemao Liu | Kaisheng Yao
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Prior methods to text segmentation are mostly at token level. Despite the adequacy, this nature limits their full potential to capture the long-term dependencies among segments. In this work, we propose a novel framework that incrementally segments natural language sentences at segment level. For every step in segmentation, it recognizes the leftmost segment of the remaining sequence. Implementations involve LSTM-minus technique to construct the phrase representations and recurrent neural networks (RNN) to model the iterations of determining the leftmost segments. We have conducted extensive experiments on syntactic chunking and Chinese part-of-speech (POS) tagging across 3 datasets, demonstrating that our methods have significantly outperformed previous all baselines and achieved new state-of-the-art results. Moreover, qualitative analysis and the study on segmenting long-length sentences verify its effectiveness in modeling long-term dependencies.

2020

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Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network
Yangming Li | Kaisheng Yao | Libo Qin | Wanxiang Che | Xiaolong Li | Ting Liu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Data-driven approaches using neural networks have achieved promising performances in natural language generation (NLG). However, neural generators are prone to make mistakes, e.g., neglecting an input slot value and generating a redundant slot value. Prior works refer this to hallucination phenomenon. In this paper, we study slot consistency for building reliable NLG systems with all slot values of input dialogue act (DA) properly generated in output sentences. We propose Iterative Rectification Network (IRN) for improving general NLG systems to produce both correct and fluent responses. It applies a bootstrapping algorithm to sample training candidates and uses reinforcement learning to incorporate discrete reward related to slot inconsistency into training. Comprehensive studies have been conducted on multiple benchmark datasets, showing that the proposed methods have significantly reduced the slot error rate (ERR) for all strong baselines. Human evaluations also have confirmed its effectiveness.

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Handling Rare Entities for Neural Sequence Labeling
Yangming Li | Han Li | Kaisheng Yao | Xiaolong Li
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

One great challenge in neural sequence labeling is the data sparsity problem for rare entity words and phrases. Most of test set entities appear only few times and are even unseen in training corpus, yielding large number of out-of-vocabulary (OOV) and low-frequency (LF) entities during evaluation. In this work, we propose approaches to address this problem. For OOV entities, we introduce local context reconstruction to implicitly incorporate contextual information into their representations. For LF entities, we present delexicalized entity identification to explicitly extract their frequency-agnostic and entity-type-specific representations. Extensive experiments on multiple benchmark datasets show that our model has significantly outperformed all previous methods and achieved new start-of-the-art results. Notably, our methods surpass the model fine-tuned on pre-trained language models without external resource.

2016

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Recurrent Support Vector Machines For Slot Tagging In Spoken Language Understanding
Yangyang Shi | Kaisheng Yao | Hu Chen | Dong Yu | Yi-Cheng Pan | Mei-Yuh Hwang
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Incorporating Structural Alignment Biases into an Attentional Neural Translation Model
Trevor Cohn | Cong Duy Vu Hoang | Ekaterina Vymolova | Kaisheng Yao | Chris Dyer | Gholamreza Haffari
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Deep LSTM based Feature Mapping for Query Classification
Yangyang Shi | Kaisheng Yao | Le Tian | Daxin Jiang
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies