# Response Retrieval

Response retrieval/selection aims to rank/select a proper response from a dialog repository. Automatic conversation (AC) aims to create an automatic human-computer dialog process for the purpose of question answering, task completion, and social chat (i.e., chit-chat). In general, AC could be formulated either as an IR problem that aims to rank/select a proper response from a dialog repository or a generation problem that aims to generate an appropriate response with respect to the input utterance. Here, we refer response retrieval as the IR-based way to do AC. Example:

## Classic Datasets

Dataset Partition #Context Response pair #Candidate per Context Positive:Negative Avg #turns per context
UDC train 1M 2 1:1 10.13
UDC validation 500k 10 1:9 10.11
UDC test 500k 10 1:9 10.11
Douban train 1M 2 1:1 6.69
Douban validation 50k 2 1:1 6.75
Douban test 10k 10 1.18:8.82 6.45
MSDialog train 173k 10 1:9 5.0
MSDialog validation 37k 10 1:9 4.9
MSDialog test 35k 10 1:9 4.4
EDC train 1M 2 1:1 5.51
EDC validation 10k 2 1:1 5.48
EDC test 10k 10 1:9 5.64
• Ubuntu Dialog Corpus (UDC) contains multi-turn dialogues collected from chat logs of the Ubuntu Forum. The data set consists of 1 million context-response pairs for training, 0.5 million pairs for validation, and 0.5 million pairs for testing. Positive responses are true responses from humans, and negative ones are randomly sampled. The ratio of the positive and the negative is 1:1 in training, and 1:9 in validation and testing.
• Douban Conversation Corpus is an open domain dataset constructed from Douban group (a popular social networking service in China). The data set consists of 1 million context-response pairs for training, 50k pairs for validation, and 10k pairs for testing, corresponding to 2, 2, and 10 response candidates per context respectively. Response candidates on the test set, retrieved from Sina Weibo (the largest microblogging service in China), are labeled by human judges.
• MSDialog is a labeled dialog dataset of question answering (QA) interactions between information seekers and answer providers from an online forum on Microsoft products (Microsoft Community). The dataset contains more than 2,000 multi-turn information-seeking conversations with 10,000 utterances that are annotated with user intent on the utterance level.
• E-commerce Dialogue Corpus contains over 5 types of conversations (e.g. commodity consultation, logistics express, recommendation, negotiation and chitchat) based on over 20 commodities. The ratio of the positive and the negative is 1:1 in training and validation, and 1:9 in testing.

$R_n@k$: recall at position $k$ in $n$ candidates.

## Performance

### Ubuntu Corpus

Model Code MAP $R_2@1$ $R_{10}@1$ $R_{10}@2$ $R_{10}@5$ Paper type
Multi-View (Zhou et al. 2016) N/A 0.908 0.662 0.801 0.951 Multi-view Response Selection for Human-Computer Conversation multi-turn
DL2R (Yan, Song and Wu 2016) N/A 0.899 0.626 0.783 0.944 Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System multi-turn
SMN (Wu et al. 2017) 0.7327 0.927 0.726 0.847 0.962 Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots Multi-turn
DAM(Zhou et al. 2018) 0.938 0.767 0.874 0.969 Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network multi-turn
DUA (Zhang et al. 2018) 0.752 0.868 0.962 Modeling Multi-turn Conversation with Deep Utterance Aggregation multi-turn
DMN (Yang et al. 2018) 0.7719 Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems multi-turn
U2U-IMN(Gu et al. 2019 a) 0.866 0.945 0.790 0.886 0.973 Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots multi-turn
TripleNet(Ma et al. 2019) 0.943 0.79 0.885 0.97 TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-based Chatbots multi-turn
IMN(Gu et al. 2019 b) 0.946 0.794 0.889 0.974 Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots multi-turn
IOI-local(Tao et al. 2019) 0.947 0.796 0.894 0.974 One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues multi-turn
MSN(Yuan et al. 2019) 0.8 0.899 0.978 Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots multi-turn
SA-BERT(Gu et al. 2020) 0.965 0.855 0.928 0.983 Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots multi-turn

### Douban Conversation Corpus

Model Code MAP MRR P@1 $R_{10}@1$ $R_{10}@2$ $R_{10}@5$ Paper type
Multi-View (Zhou et al. 2016) N/A 0.505 0.543 0.342 0.202 0.350 0.729 Multi-view Response Selection for Human-Computer Conversation multi-turn
DL2R (Yan, Song and Wu 2016) N/A 0.488 0.527 0.33 0.193 0.342 0.705 Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System multi-turn
SMN (Wu et al. 2017) 0.529 0.572 0.397 0.236 0.396 0.734 Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots Multi-turn
DAM(Zhou et al. 2018) 0.55 0.601 0.427 0.254 0.410 0.757 Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network multi-turn
DUA (Zhang et al. 2018) 0.551 0.599 0.421 0.243 0.421 0.780 Modeling Multi-turn Conversation with Deep Utterance Aggregation multi-turn
U2U-IMN(Gu et al. 2019 a) 0.564 0.611 0.429 0.259 0.43 0.791 Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots multi-turn
TripleNet(Ma et al. 2019) 0.564 0.618 0.447 0.268 0.426 0.778 TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-based Chatbots multi-turn
IMN(Gu et al. 2019 b) 0.570 0.615 0.433 0.262 0.452 0.789 Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots multi-turn
IOI-local(Tao et al. 2019) 0.573 0.621 0.444 0.269 0.451 0.786 One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues multi-turn
MSN(Yuan et al. 2019) 0.587 0.632 0.470 0.295 0.452 0.788 Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots multi-turn
SA-BERT(Gu et al. 2020) 0.619 0.659 0.496 0.313 0.481 0.847 Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots multi-turn

### MSDialog

Model Code MAP Recall@5 Recall@1 Recall@2 Paper type
DMN (Yang et al. 2018) 0.6792 0.9356 0.5021 0.7122 Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems multi-turn

### E-commerce Corpus

Model Code MAP $R_{10}@1$ $R_{10}@2$ $R_{10}@5$ Paper type
Multi-View (Zhou et al. 2016) N/A 0.421 0.601 0.861 Multi-view Response Selection for Human-Computer Conversation multi-turn
DL2R (Yan, Song and Wu 2016) N/A 0.399 0.571 0.842 Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System multi-turn
SMN (Wu et al. 2017) 0.453 0.654 0.886 Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots Multi-turn
DAM(Zhou et al. 2018) 0.526 0.727 0.933 Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network multi-turn
DUA (Zhang et al. 2018) 0.501 0.700 0.921 Modeling Multi-turn Conversation with Deep Utterance Aggregation multi-turn
U2U-IMN(Gu et al. 2019 a) 0.759 0.616 0.806 0.966 Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots multi-turn
IMN(Gu et al. 2019 b) 0.621 0.797 0.964 Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots multi-turn
IOI-local(Tao et al. 2019) 0.563 0.768 0.950 One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues multi-turn
MSN(Yuan et al. 2019) 0.606 0.770 0.937 Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots multi-turn
SA-BERT(Gu et al. 2020) 0.704 0.879 0.985 Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots multi-turn

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