Sequential Matching Network: A New Architecture for Multi-turn
            Response Selection in Retrieval-Based Chatbots

Wu, Yu; Wu, Wei; Chen, Xing; Zhou, Ming; Li, Zhoujun

doi:10.18653/v1/p17-1046

Cited by 434 publications

(565 citation statements)

References 21 publications

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“…, Q T u 2 } and then transforms into a sequence of hidden states. In this paper, we employ the gated recurrent unit (GRU) [7] as the cell of fusion RNN, which is popular in sequential modeling [12,40]:…”

Section: Fusion Rnn Fusion Rnn First Reads the Interaction Resultsmentioning

confidence: 99%

“…(3) SMN: [40] proposes a sequential matching network to address response selection for the multi-turn conversation problem. SMN first matches a response with each utterance in the context.…”

Section: Comparison Methodsmentioning

confidence: 99%

“…Previous works include [46], which uses an RNN to represent context and response, and measure their relevance. More recently, [40] matches a response with each utterance in the context on multiple levels of granularity, and the vectors are then combined through an RNN. The final matching score is calculated by the hidden states of the RNN.…”

Section: Related Workmentioning

confidence: 99%

“…Before presenting our approach for sticker response selection in multi-turn dialog, we first introduce our notations and key concepts. Similar to the multi-turn dialog response selection [40,47], we assume that there is a multi-turn dialog context s = {u 1 , . .…”

Section: Problem Formulationmentioning

confidence: 99%

See 3 more Smart Citations

Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog

Gao

Chen

Liu

et al. 2020

Proceedings of the Web Conference 2020

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Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching text labels of stickers with previous utterances. However, due to their large quantities, it is impractical to require text labels for the all stickers. Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context history without any external labels. Two main challenges are confronted in this task. One is to learn semantic meaning of stickers without corresponding text labels. Another challenge is to jointly model the candidate sticker with the multi-turn dialog context. To tackle these challenges, we propose a sticker response selector (SRS) model. Specifically, SRS first employs a convolutional based sticker image encoder and a self-attention based multi-turn dialog encoder to obtain the representation of stickers and utterances. Next, deep interaction network is proposed to conduct deep matching between the sticker with each utterance in the dialog history. SRS then learns the short-term and long-term dependency between all interaction results by a fusion network to output the the final matching score. To evaluate our proposed method, we collect a large-scale realworld dialog dataset with stickers from one of the most popular online chatting platform. Extensive experiments conducted on this dataset show that our model achieves the state-of-the-art performance for all commonly-used metrics. Experiments also verify the * Equal contribution. Ordering is decided by a coin flip. Work performed during an internship at IIAI. † WICT is the abbreviation of Wangxuan Institute of Computer Technology. effectiveness of each component of SRS. To facilitate further research in sticker selection field, we release this dataset of 340K multi-turn dialog and sticker pairs 1 .

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Section: Fusion Rnn Fusion Rnn First Reads the Interaction Resultsmentioning

confidence: 99%

Section: Comparison Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

See 2 more Smart Citations

Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog

Gao

Chen

Liu

et al. 2020

Proceedings of the Web Conference 2020

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“…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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A taxonomy, data set, and benchmark for detecting and classifying malevolent dialogue responses

Zhang

Ren

Rijke

2021

Asso for Info Science & Tech

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Conversational interfaces are increasingly popular as a way of connecting people to information. With the increased generative capacity of corpus‐based conversational agents comes the need to classify and filter out malevolent responses that are inappropriate in terms of content and dialogue acts. Previous studies on the topic of detecting and classifying inappropriate content are mostly focused on a specific category of malevolence or on single sentences instead of an entire dialogue. We make three contributions to advance research on the malevolent dialogue response detection and classification (MDRDC) task. First, we define the task and present a hierarchical malevolent dialogue taxonomy. Second, we create a labeled multiturn dialogue data set and formulate the MDRDC task as a hierarchical classification task. Last, we apply state‐of‐the‐art text classification methods to the MDRDC task, and report on experiments aimed at assessing the performance of these approaches.

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Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots

Cited by 434 publications

References 21 publications

Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog

Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog

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

A taxonomy, data set, and benchmark for detecting and classifying malevolent dialogue responses

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