Multi-view Response Selection for Human-Computer Conversation

Zhou, Xiangyang; Dong, Daxiang; Wu, Hua; Zhao, Shenghui; Yu, Dianhai; Tian, Hao; Li, Xuan; Yan, Rui

doi:10.18653/v1/d16-1036

Cited by 209 publications

(178 citation statements)

References 18 publications

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“…For example, Luong et al (2015) proposes global and local attention networks for machine translation, while others investigate hierarchical attention networks for document classification (Yang et al, 2016), sentiment classification , and dialog response selection (Zhou et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Deep Communicating Agents for Abstractive Summarization

Çelikyılmaz

Bosselut

et al. 2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

295

253

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We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization. With deep communicating agents, the task of encoding a long text is divided across multiple collaborating agents, each in charge of a subsection of the input text. These encoders are connected to a single decoder, trained end-to-end using reinforcement learning to generate a focused and coherent summary. Empirical results demonstrate that multiple communicating encoders lead to a higher quality summary compared to several strong baselines, including those based on a single encoder or multiple non-communicating encoders.

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Section: Related Workmentioning

confidence: 99%

Deep Communicating Agents for Abstractive Summarization

Çelikyılmaz

Bosselut

et al. 2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

295

253

View full text Add to dashboard Cite

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“…Earlier works focused on paired word sequences only, while Zhou et al (2016) and Iulian et al (2017) have demonstrated that the comprehensibility of the generated responses can benefit from multiview training with respect to words, coarse tokens and utterances. Moreover, Sordoni et al (2015) proposed a context-aware response generation model that goes beyond single-turn conversations.…”

Section: Related Workmentioning

confidence: 99%

Neural Response Generation via GAN with an Approximate Embedding Layer

Xu¹,

Liu²,

Wang³

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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This paper presents a Generative Adversarial Network (GAN) to model singleturn short-text conversations, which trains a sequence-to-sequence (Seq2Seq) network for response generation simultaneously with a discriminative classifier that measures the differences between human-produced responses and machinegenerated ones. In addition, the proposed method introduces an approximate embedding layer to solve the non-differentiable problem caused by the sampling-based output decoding procedure in the Seq2Seq generative model. The GAN setup provides an effective way to avoid noninformative responses (a.k.a "safe responses"), which are frequently observed in traditional neural response generators. The experimental results show that the proposed approach significantly outperforms existing neural response generation models in diversity metrics, with slight increases in relevance scores as well, when evaluated on both a Mandarin corpus and an English corpus.

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“…Ubuntu E-commerce R@1 R@2 R@5 R@1 R@2 R@5 TF-IDF (Lowe et al, 2015) 0.410 0.545 0.708 0.159 0.256 0.477 RNN (Lowe et al, 2015) 0.403 0.547 0.819 0.325 0.463 0.775 CNN (Kadlec et al, 2015) 0.549 0.684 0.896 0.328 0.515 0.792 LSTM (Kadlec et al, 2015) 0.638 0.784 0.949 0.365 0.536 0.828 BiLSTM (Kadlec et al, 2015) 0.630 0.780 0.944 0.355 0.525 0.825 MV-LSTM (Wan et al, 2016) 0.653 0.804 0.946 0.412 0.591 0.857 Match-LSTM (Wang and Jiang, 2016) 0.653 0.799 0.944 0.410 0.590 0.858 Attentive-LSTM (Tan et al, 2015) 0.633 0.789 0.943 0.401 0.581 0.849 Multi-Channel (Wu et al, 2017) 0.656 0.809 0.942 0.422 0.609 0.871 Multi-View (Zhou et al, 2016) 0.662 0.801 0.951 0.421 0.601 0.861 DL2R 0.626 0.783 0.944 0.399 0.571 0.842 SMN (Wu et al, 2017) 0.726 0.847 0.961 0.453 0.654 0.886 DUA (Zhang et al, 2018) 0.752 0.868 0.962 0.501 0.700 0.921 DAM (Zhou et al, 2018) 0.767 0.874 0.969 ---Our ESIM 0.796 0.894 0.975 0.570 0.767 0.948 Table 5: Comparisons of different models on two large-scale public benchmark datasets. All the results except ours are cited from the previous works (Zhang et al, 2018;Zhou et al, 2018).…”

Section: Modelsmentioning

confidence: 99%

“…Retrieval-based methods select the best response from a candidate pool for the multi-turn context, which can be considered as performing a multi-turn response selection task. The typical approaches for multi-turn response selection mainly consist of sequence-based methods (Lowe et al, 2015;Yan et al, 2016) and hierarchy-based methods (Zhou et al, 2016;Wu et al, 2017;Zhang et al, 2018;Zhou et al, 2018). Sequence-based methods usually concatenate the context utterances into a long sequence.…”

Section: Introductionmentioning

confidence: 99%

Sequential neural networks for noetic end-to-end response selection

Chen

Wang

2020

Computer Speech & Language

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The noetic end-to-end response selection challenge as one track in the 7th Dialog System Technology Challenges (DSTC7) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for the multi-turn context. This paper presents our systems that are ranked top 1 on both datasets under this challenge, one focused and small (Advising) and the other more diverse and large (Ubuntu). Previous state-of-the-art models use hierarchybased (utterance-level and token-level) neural networks to explicitly model the interactions among different turns' utterances for context modeling. In this paper, we investigate a sequential matching model based only on chain sequence for multi-turn response selection. Our results demonstrate that the potentials of sequential matching approaches have not yet been fully exploited in the past for multi-turn response selection. In addition to ranking top 1 in the challenge, the proposed model outperforms all previous models, including state-of-the-art hierarchy-based models, on two large-scale public multi-turn response selection benchmark datasets. Keywords:DSTC7, response selection, ESIM, BERT, end-to-end, sequential matching approaches 1. We develop an Enhanced Sequential Inference Model (ESIM) based system for the DSTC7 noetic end-to-end response selection track. On top of the ESIM model, we explore methods for exploiting multiple word embeddings, heuristic data augmentation, tuning the ratio between positive and negative samples, and emphasizing the importance of the most recent context utterances. 2. We propose a two-step approach for selecting the next utterance from a large amount of candidates (i.e., for subtask 2 on the Ubuntu dataset, we need to select the next utterance from a candidate pool of 120,000 sentences), by first using a sentence-encoding based method to select the top N candidates from the large set of candidates and then reranking them using ESIM, achieving a high performance with an acceptable overall computational cost. 3. We conduct systematic ablation analysis of the above-mentioned methods for enhancing the ESIM model performance. In particular, we develop effective and efficient model ensemble by averaging the output from models

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Multi-view Response Selection for Human-Computer Conversation

Cited by 209 publications

References 18 publications

Deep Communicating Agents for Abstractive Summarization

Deep Communicating Agents for Abstractive Summarization

Neural Response Generation via GAN with an Approximate Embedding Layer

Sequential neural networks for noetic end-to-end response selection

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