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This paper provides an extensive survey of currently available datasets suitable for research, development, and evaluation of data-driven dialogue systems

A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version.

D&D, no. 1 (2018): 1-49

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摘要

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent re...更多

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数据

简介
  • Known as interactive conversational agents, virtual agents or sometimes chatterbots, are useful in a wide range of applications ranging from technical support services to language learning tools and entertainment (Young et al, 2013; Shawar and Atwell, 2007b).
  • The use of machine learning methods — such as neural networks — require an understanding of the availability, requirements, and uses of available dialogue corpora.
  • To this end, this paper presents a broad survey of available dialogue corpora
重点内容
  • Dialogue systems, also known as interactive conversational agents, virtual agents or sometimes chatterbots, are useful in a wide range of applications ranging from technical support services to language learning tools and entertainment (Young et al, 2013; Shawar and Atwell, 2007b)
  • A wide range of datadriven machine learning methods have been shown to be effective for natural language processing, including tasks relevant to dialogue, such as dialogue act classification (Reithinger and Klesen, 1997; Stolcke et al, 2000), dialogue state tracking (Thomson and Young, 2010; Wang and Lemon, 2013; Ren et al, 2013; Henderson et al, 2013; Williams et al, 2013; Henderson et al, 2014c; Kim et al, 2015), natural language generation (Langkilde and Knight, 1998; Oh and Rudnicky, 2000; Walker et al, 2002; Ratnaparkhi, 2002; Stent et al, 2004; Rieser and Lemon, 2010; Mairesse et al, 2010; Mairesse and Young, 2014; Wen et al, 2015a; Sharma et al, 2016), and dialogue policy learning (Young et al, 2013)
  • This paper provides an extensive survey of currently available datasets suitable for research, development, and evaluation of data-driven dialogue systems
  • Neural networks can be applied to narrow domains, such as restaurant recommendation, with relatively little data (Wen et al, 2017)
  • To obtain reasonable results in such a setting, neural network practitioners have resorted to training neural network models on datasets with hundreds of thousands to millions of dialogues: the Twitter Corpus (Ritter et al, 2010; Sordoni et al, 2015), Reddit, the Ubuntu Dialogue Corpus (Lowe et al, 2015a), and various movie subtitle datasets such as SubTle, OpenSubtitles, Movie-DiC, and the Movie Dialogue Dataset (Ameixa and Coheur, 2013; Tiedemann, 2012; Banchs, 2012; Dodge et al, 2015)
  • While the conversation topics in these datasets often vary considerably, the nature of the datasets themselves are fairly fixed in the form of informal written dialogues between humans. This is the case for movie scripts, forum posts, and micro-blogging platforms. Learning only from these sources will bias dialogue systems towards certain kinds of interactions and behaviours; for example, written corpora usually have a specific turn-taking structure that is different from spoken conversation, and they may encode biases against certain groups or populations (Henderson et al, 2017)
方法
  • Processing (EMNLP), 2011.
  • S. Rosenthal and K.
  • The author couldnts agree more: The role of conversational structure in agreement and disagreement detection in online discussions.
  • In Special Interest Group on Discourse and Dialogue (SIGDIAL), 2015.
  • S. Rosset and S.
  • The ritel corpus-an annotated human-machine open-domain question answering spoken dialog corpus.
  • In The International Conference on Language Resources and Evaluation (LREC), 2006.
  • A. Roy, C.
  • Guinaudeau, H.
  • TVD: a reproducible and multiply aligned tv series dataset.
结果
  • Evaluation metrics

    One of the most challenging aspects of constructing dialogue systems lies in their evaluation.
  • Often it is necessary to optimize performance on a pseudo-performance metric prior to release
  • This is true if a dialogue model has many hyper-parameters to be optimized — it is infeasible to run user experiments for every parameter setting in a grid search.
  • The authors would have some automated metrics for calculating a score for each model, and only involve human evaluators once the best model has been chosen with reasonable confidence
结论
  • The authors discuss a number of challenges and general methods related to the development and evaluation of data-driven dialogue systems.
  • Evaluating a data-driven dialogue system properly is critical for real-world deployments as well as for advancing state-of-the-art research, in which case reproducibility of methods and results is crucial.This paper provides an extensive survey of currently available datasets suitable for research, development, and evaluation of data-driven dialogue systems
  • The authors categorize these corpora along several dimensions depending on whether the dataset is written or spoken, between human interlocutors or human-machine conversations, and constrained in topic or more free-form.
  • There is a lack of large-scale multi-modal datasets, which may be crucial towards grounding the language learned by the dialogue agents in human-like experience
表格
  • Table1: Human-machine dialogue datasets. Starred (*) numbers are approximated based on the average number of words per utterance. Datasets marked with (†) indicate Wizard-of-Oz dialogues, where the machine is secretly operated by a human
  • Table2: Human-human spontaneous spoken dialogue datasets. Starred (*) numbers are estimates based on the average rate of English speech from the National Center for Voice and Speech (www.ncvs.org/ncvs/tutorials/voiceprod/tutorial/quality.html)
  • Table3: Human-human constrained spoken dialogue datasets. Starred (*) numbers are estimates based on the average rate of English speech from the National Center for Voice and Speech (www.ncvs.org/ncvs/tutorials/voiceprod/tutorial/quality.html)
  • Table4: Human-human scripted dialogue datasets. Quantities denoted with (†) indicate estimates based on average number of dialogues per movie (<a class="ref-link" id="cBanchs_2012_a" href="#rBanchs_2012_a">Banchs, 2012</a>) and the number of scripts or works in the corpus. Dialogues may not be explicitly separated in these datasets. TV show datasets were adjusted based on the ratio of average film runtime (112 minutes) to average TV show runtime (36 minutes). This data was scraped from the IMBD database (http://www.imdb.com/interfaces). ( Starred (*) quantities are estimated based on the average number of words and utterances per film, and the average lengths of films and TV shows. Estimates derived from the Tameri Guide for Writers (http://www.tameri.com/format/wordcounts.html)
  • Table5: Human-human written dialogue datasets. Starred (*) quantities are computed using word counts based on spaces. Triangle ( ) indicates lower and upper bounds computed using average words per utterance estimated on a similar Reddit corpus Schrading (2015). Square (2) indicates estimates based only on the English part of the corpus. Dialogues indicated by (†) are contiguous blocks of recorded conversation in a multiparticipant chat. For UseNet, the average number of turns are calculated as the average number of posts collected per newsgroup. (‡) indicates an estimate based on a Twitter dataset of similar size and refers to tokens as well as words
Download tables as Excel
基金
  • The authors gratefully acknowledge financial support by the Samsung Advanced Institute of Technology (SAIT), the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canada Research Chairs, the Canadian Institute for Advanced Research (CIFAR) and Compute Canada
  • The second author is funded by a Vanier Graduate Scholarship
研究对象与分析
humans: 2
Human-Machine Corpora. Another salient distinction between dialogue datasets resides in the types of interlocutors — notably, whether it involves interactions between two humans, or between a human and a computer.2. The distinction is important because current artificial dialogue systems are significantly constrained

small datasets with the same number of dialogues in the: 2
The second comes from a statistical natural language processing perspective: since the statistical complexity of a corpus grows with the linguistic diversity and number of topics, the number of examples required by a machine learning algorithm to model the patterns in it will also grow with the linguistic diversity and number of topics. Consider two small datasets with the same number of dialogues in the domain of bus schedule information: in one dataset the conversations between the users and operator is natural, and the operator can improvise and chitchat; in the other dataset, the operator reads from a script to provide the bus information. Despite having the same size, the second dataset will have less linguistic diversity and not include chitchat topics

humans: 2
4.1 Human-Machine Corpora. As discussed in Subsection 3.2, an important distinction between dialogue datasets is whether they consist of dialogues between two humans or between a human and a machine. Thus, we begin by outlining some of the existing human-machine corpora in several categories based on the types of systems the humans interact with: Restaurant and Travel Information, Open-Domain Knowledge Retrieval, and Other Specialized systems

datasets: 3
That is, to form a coherent dialogue, previous contexts must be accounted for – either explicitly or in an end-to-end manner. As such, the first three datasets in the DSTC — referred to as DSTC1, DSTC2, and DSTC3 respectively — are medium-sized spoken datasets obtained from human-machine interactions with restaurant and travel information systems. All datasets provide labels specifying the current goal and desired action of the system

young and old adults: 50
The latter was transcribed and annotated with simple speech acts such as “signaling emotions” or “self-addressing”. The MATCH corpus (Georgila et al, 2010) is a small corpus of 447 dialogues based on a Wizard-of-Oz experiment, which contains conversations from 50 young and old adults interacting with spoken dialogue systems. These conversations were annotated semi-automatically with dialogue acts and “Information State Update” (ISU) representations of dialogue context

participants: 2
This dataset consists of approximately 2,500 dialogues from phone calls, along with wordby-word transcriptions, with about 500 different speakers. A computer-driven robot operator system introduced a topic for discussion between two participants, and recorded the resulting conversation. About 70 casual topics were provided, of which about 50 were frequently used

participants: 4
While the original dataset featured 2D visual feeds, an updated version with 3D video has also been derived, called the 4D Cardiff Conversation Database (4D CCDb) (Vandeventer et al, 2015). This version contains 17 one-minute conversations from 4 participants on similarly un-constrained topics. The Diachronic Corpus of Present-Day Spoken English (DCPSE) (Aarts and Wallis, 2006) is a parsed corpus of spoken English made up of two separate datasets

separate datasets: 2
This version contains 17 one-minute conversations from 4 participants on similarly un-constrained topics. The Diachronic Corpus of Present-Day Spoken English (DCPSE) (Aarts and Wallis, 2006) is a parsed corpus of spoken English made up of two separate datasets. It contains more than 400,000 words from the ICE-GB corpus (collected in the early 1990s) and 400,000 words from the LondonLund Corpus (collected from the late 1960s to the early 1980s)

dialogues between people: 36
By adding these controls, the dataset attempts to focus on solely the dialogue and human speech involved in the planning process. The Walking Around Corpus (Brennan et al, 2013) consists of 36 dialogues between people communicating over mobile telephone. The dialogues have two parts: first, a ‘stationary partner’ is asked to direct a ‘mobile partner’ to find 18 destinations on a medium-sized university campus

people: 2
It contains 8 long dialogues, totalling about 30 minutes each. Since the persuadees often either disagree or agree strongly with the persuaders points, this would be good corpus for studying social signs of (dis)-agreement between two people. The MAHNOB Mimicry Database (Sun et al, 2011) contains 11 hours of recordings, split over 54 sessions between 60 people engaged either in a socio-political discussion or negotiating a tenancy agreement

people: 60
Since the persuadees often either disagree or agree strongly with the persuaders points, this would be good corpus for studying social signs of (dis)-agreement between two people. The MAHNOB Mimicry Database (Sun et al, 2011) contains 11 hours of recordings, split over 54 sessions between 60 people engaged either in a socio-political discussion or negotiating a tenancy agreement. This dataset consists of a set of fully synchronized audio-visual recordings of natural dyadic (one-on-one) interactions

people: 12
The IDIAP Wolf Corpus (Hung and Chittaranjan, 2010) is an audio-visual corpus containing natural conversational data of volunteers who took part in an adversarial role-playing game called ‘Werewolf’. Four groups of 8 to 12 people were recorded using headset microphones and synchronized video cameras, resulting in over 7 hours of conversational data. The novelty of this dataset is that the roles of other players are unknown to game participants, and some of the roles are deceptive in nature

people: 2
The latter consists of a mix of British and American film scripts, while the former consists of solely American films. The majority of these datasets consist of raw scripts, which are not guaranteed to portray conversations between only two people. The dataset collected by Nio et al (2014), which we refer to as the Filtered Movie Script Corpus, takes over 1 million utterance-response pairs from web-based script resources and filters them down to 86,000 such pairs

utterance-response pairs: 1000000
The majority of these datasets consist of raw scripts, which are not guaranteed to portray conversations between only two people. The dataset collected by Nio et al (2014), which we refer to as the Filtered Movie Script Corpus, takes over 1 million utterance-response pairs from web-based script resources and filters them down to 86,000 such pairs. The filtering method limits the extracted utterances to X-Y-X triples, where X is spoken by one actor and Y by another, and each of the utterances share some semantic similarity

primary movie subtitle datasets: 2
Thus, this dataset could be useful for building dialogue personalization models. There are two primary movie subtitle datasets: the OpenSubtitles (Tiedemann, 2012) and the SubTle Corpus (Ameixa and Coheur, 2013). Both corpora are based on the OpenSubtitles website.9

post-reply pairs: 1300000
The NPS Internet Chatroom Conversations Corpus was one of the first corpora of computer-mediated communication (CMC), and it was intended for various NLP applications such as conversation thread topic detection, author profiling, entity identification, and social network analysis. Several corpora of spontaneous micro-blogging conversations have been collected, such as the Twitter Corpus from Ritter et al (2010), which contains 1.3 million post-reply pairs extracted from Twitter. The corpus was originally constructed to aid in the production of unsupervised approaches to modeling dialogue acts

pairs: 4232
The Twitter Triples Corpus (Sordoni et al, 2015) is one such example, with a described original dataset of 127 million context-message-response triples, but only a small labeled subset of this corpus has been released. Specifically, the released labeled subset contains 4,232 pairs that scored an average of greater than 4 on the Likert-type scale by crowdsourced evaluators for quality of the response to the contextmessage pair. Similarly, a large micro-blogging dataset, the Sina Weibo Corpus (Shang et al, 2015), which contains 4.5 million post-reply pairs, has been collected and used in literature, but this resource has not yet been made publicly available

post-reply pairs: 4500000
Specifically, the released labeled subset contains 4,232 pairs that scored an average of greater than 4 on the Likert-type scale by crowdsourced evaluators for quality of the response to the contextmessage pair. Similarly, a large micro-blogging dataset, the Sina Weibo Corpus (Shang et al, 2015), which contains 4.5 million post-reply pairs, has been collected and used in literature, but this resource has not yet been made publicly available. We do not include the Sina Weibo Corpus (and its derivatives) in the tables in this section, as they are not primarily in English

users: 2
10. http://www.reddit.com 11. http://www.twitter.com 12. http://www.usenet.net corpus derived from these posts has been used for research in collaborative filtering (Konstan et al, 1997) and role detection (Fisher et al, 2006). The NUS SMS Corpus (Chen and Kan, 2013) consists of conversations carried out over mobile phone SMS messages between two users. While the original purpose of the dataset was to improve predictive text entry when mobile phones still mapped multiple letters to a single number, aided by video and timing analysis of users entering their messages it could equally be used for analysis of informal dialogue

users: 2
UseNet forum postings. SMS messages collected between two users, with timing analysis. 1.7B comments across Reddit. Reddit posts from either domestic abuse subreddits, or general chat

posts: 390000
The difference between the two corpora is the source: the former is collected from Create Debate forums and the latter from a mix of Wikipedia Discussion pages and LiveJournal postings. The Internet Argument Corpus (IAC) (Walker et al, 2012b) is a forum-based corpus with 390,000 posts on 11,000 discussion topics. Each topic is controversial in nature, including subjects such as evolution, gay marriage and climate change; users participate by sharing their opinions on one of these topics

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作者
Iulian Vlad Serban
Iulian Vlad Serban
Peter Henderson
Peter Henderson
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