Dialogue

The Reddit dataset is a graph dataset from Reddit posts made in the month of September, 2014. The node label in this case is the community, or “subreddit”, that a post belongs to. 50 large communities have been sampled to build a post-to-post graph, connecting posts if the same user comments on both. In total this dataset contains 232,965 posts with an average degree of 492. The first 20 days are used for training and the remaining days for testing (with 30% used for validation). For features, off-the-shelf 300-dimensional GloVe CommonCrawl word vectors are used. Source: https://arxiv.org/pdf/1706.02216.pdf Image Source: https://minimaxir.com/2016/05/reddit-graph/

The Multi-domain Wizard-of-Oz (MultiWOZ) dataset is a large-scale human-human conversational corpus spanning over seven domains, containing 8438 multi-turn dialogues, with each dialogue averaging 14 turns. Different from existing standard datasets like WOZ and DSTC2, which contain less than 10 slots and only a few hundred values, MultiWOZ has 30 (domain, slot) pairs and over 4,500 possible values. The dialogues span seven domains: restaurant, hotel, attraction, taxi, train, hospital and police. Source: Contents Image Source: Zhang et al

OpenSubtitles is collection of multilingual parallel corpora. The dataset is compiled from a large database of movie and TV subtitles and includes a total of 1689 bitexts spanning 2.6 billion sentences across 60 languages.

The Schema-Guided Dialogue (SGD) dataset consists of over 20k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 20 domains, ranging from banks and events to media, calendar, travel, and weather. For most of these domains, the dataset contains multiple different APIs, many of which have overlapping functionalities but different interfaces, which reflects common real-world scenarios. The wide range of available annotations can be used for intent prediction, slot filling, dialogue state tracking, policy imitation learning, language generation, user simulation learning, among other tasks in large-scale virtual assistants. Besides these, the dataset has unseen domains and services in the evaluation set to quantify the performance in zero-shot or few shot settings. Source: The Schema-Guided Dialogue Dataset

Visual Dialog (VisDial) dataset contains human annotated questions based on images of MS COCO dataset. This dataset was developed by pairing two subjects on Amazon Mechanical Turk to chat about an image. One person was assigned the job of a ‘questioner’ and the other person acted as an ‘answerer’. The questioner sees only the text description of an image (i.e., an image caption from MS COCO dataset) and the original image remains hidden to the questioner. Their task is to ask questions about this hidden image to “imagine the scene better”. The answerer sees the image, caption and answers the questions asked by the questioner. The two of them can continue the conversation by asking and answering questions for 10 rounds at max. VisDial v1.0 contains 123K dialogues on MS COCO (2017 training set) for training split, 2K dialogues with validation images for validation split and 8K dialogues on test set for test-standard set. The previously released v0.5 and v0.9 versions of VisDial dataset (corresponding to older splits of MS COCO) are considered deprecated. Source: Granular Multimodal Attention Networks for Visual Dialog Image Source: https://arxiv.org/pdf/1611.08669.pdf

A new open-vocabulary language modelling benchmark derived from books. Source: Compressive Transformers for Long-Range Sequence Modelling

Wizard of Wikipedia is a large dataset with conversations directly grounded with knowledge retrieved from Wikipedia. It is used to train and evaluate dialogue systems for knowledgeable open dialogue with clear grounding

We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark. FLEURS is an n-way parallel speech dataset in 102 languages built on top of the machine translation FLoRes-101 benchmark, with approximately 12 hours of speech supervision per language. FLEURS can be used for a variety of speech tasks, including Automatic Speech Recognition (ASR), Speech Language Identification (Speech LangID), Translation and Retrieval. In this paper, we provide baselines for the tasks based on multilingual pre-trained models like mSLAM. The goal of FLEURS is to enable speech technology in more languages and catalyze research in low-resource speech understanding.

A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets. Source: SLURP: A Spoken Language Understanding Resource Package

The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset. The speaker pairs each have assigned profiles coming from a set of 1155 possible personas (at training time), each consisting of at least 5 profile sentences, setting aside 100 never seen before personas for validation. As the original PERSONA-CHAT test set was released, a new hidden test set consisted of 100 new personas and over 1,015 dialogs was created by crowdsourced workers. To avoid modeling that takes advantage of trivial word overlap, additional rewritten sets of the same train and test personas were crowdsourced, with related sentences that are rephrases, generalizations or specializations, rendering the task much more challenging. For example “I just got my nails done” is revised as “I love to pamper myself on a regular basis” and “I am on a diet now” is revised as “I need to lose weight.” The training, validation and hidden test sets consists of 17,878, 1,000 and 1,015 dialogues, respectively. Source: The Second Conversational Intelligence Challenge (ConvAI2) Image Source: The Second Conversational Intelligence Challenge (ConvAI2)

GuessWhat?! is a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images. GuessWhat?! is a cooperative two-player game in which both players see the picture of a rich visual scene with several objects. One player – the oracle – is randomly assigned an object (which could be a person) in the scene. This object is not known by the other player – the questioner – whose goal it is to locate the hidden object. To do so, the questioner can ask a series of yes-no questions which are answered by the oracle. Source: https://paperswithcode.com/paper/guesswhat-visual-object-discovery-through Image Source: Vries et al

The EmpatheticDialogues dataset is a large-scale multi-turn empathetic dialogue dataset collected on the Amazon Mechanical Turk, containing 24,850 one-to-one open-domain conversations. Each conversation was obtained by pairing two crowd-workers: a speaker and a listener. The speaker is asked to talk about the personal emotional feelings. The listener infers the underlying emotion through what the speaker says and responds empathetically. The dataset provides 32 evenly distributed emotion labels. Source: Empathetic Dialogue Generation viaKnowledge Enhancing and Emotion Dependency Modeling Image Source: Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset

DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 dialogues with corresponding manually labeled summaries and topics. This work is accepted by ACL findings 2021. You may find the paper here: https://arxiv.org/pdf/2105.06762.pdf. If you want to use our dataset, please cite our paper. #### Dialogue Data We collect dialogue data for DialogSum from three public dialogue corpora, namely Dailydialog (Li et al., 2017), DREAM (Sun et al., 2019) and MuTual (Cui et al., 2019), as well as an English speaking practice website. These datasets contain face-to-face spoken dialogues that cover a wide range of daily-life topics, including schooling, work, medication, shopping, leisure, travel. Most conversations take place between friends, colleagues, and between service providers and customers. Compared with previous datasets, dialogues from DialogSum have distinct characteristics: * Under rich real-life scenarios, including more diverse task-oriented scenarios; * Have clear communication patterns and intents, which is valuable to serve as summarization sources; * Have a reasonable length, which comforts the purpose of automatic summarization. #### Summaries We ask annotators to summarize each dialogue based on the following criteria: * Convey the most salient information; * Be brief; * Preserve important named entities within the conversation; * Be written from an observer perspective; * Be written in formal language. #### Topics In addition to summaries, we also ask annotators to write a short topic for each dialogue, which can be potentially useful for future work, e.g. generating summaries by leveraging topic information. Image source: https://arxiv.org/pdf/2105.06762.pdf

Fluent Speech Commands is an open source audio dataset for spoken language understanding (SLU) experiments. Each utterance is labeled with "action", "object", and "location" values; for example, "turn the lights on in the kitchen" has the label {"action": "activate", "object": "lights", "location": "kitchen"}. A model must predict each of these values, and a prediction for an utterance is deemed to be correct only if all values are correct. The task is very simple, but the dataset is large and flexible to allow for many types of experiments: for instance, one can vary the number of speakers, or remove all instances of a particular sentence and test whether a model trained on the remaining sentences can generalize.

MuTual is a retrieval-based dataset for multi-turn dialogue reasoning, which is modified from Chinese high school English listening comprehension test data. It tests dialogue reasoning via next utterance prediction. Source: https://github.com/Nealcly/MuTual Image Source: https://github.com/Nealcly/MuTual

OpenDialKG contains utterance from 15K human-to-human role-playing dialogs is manually annotated with ground-truth reference to corresponding entities and paths from a large-scale KG with 1M+ facts. Source: OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs

Ubuntu Dialogue Corpus (UDC) is a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data. The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter. Source: The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems

CoSQL is a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+ annotated SQL queries, obtained from a Wizard-of-Oz (WOZ) collection of 3k dialogues querying 200 complex DBs spanning 138 domains. Each dialogue simulates a real-world DB query scenario with a crowd worker as a user exploring the DB and a SQL expert retrieving answers with SQL, clarifying ambiguous questions, or otherwise informing of unanswerable questions. Source: CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases

For goal-oriented document-grounded dialogs, it often involves complex contexts for identifying the most relevant information, which requires better understanding of the inter-relations between conversations and documents. Meanwhile, many online user-oriented documents use both semi-structured and unstructured contents for guiding users to access information of different contexts. Thus, we create a new goal-oriented document-grounded dialogue dataset that captures more diverse scenarios derived from various document contents from multiple domains such ssa.gov and studentaid.gov. For data collection, we propose a novel pipeline approach for dialogue data construction, which has been adapted and evaluated for several domains.

The Dialog State Tracking Challenges 2 & 3 (DSTC2&3) were research challenge focused on improving the state of the art in tracking the state of spoken dialog systems. State tracking, sometimes called belief tracking, refers to accurately estimating the user's goal as a dialog progresses. Accurate state tracking is desirable because it provides robustness to errors in speech recognition, and helps reduce ambiguity inherent in language within a temporal process like dialog. In these challenges, participants were given labelled corpora of dialogs to develop state tracking algorithms. The trackers were then evaluated on a common set of held-out dialogs, which were released, un-labelled, during a one week period. The corpus was collected using Amazon Mechanical Turk, and consists of dialogs in two domains: restaurant information, and tourist information. Tourist information subsumes restaurant information, and includes bars, cafés etc. as well as multiple new slots. There were two rounds of evaluation using this data: DSTC 2 released a large number of training dialogs related to restaurant search. Compared to DSTC (which was in the bus timetables domain), DSTC 2 introduces changing user goals, tracking 'requested slots' as well as the new restaurants domain. Results from DSTC 2 were presented at SIGDIAL 2014. DSTC 3 addressed the problem of adaption to a new domain - tourist information. DSTC 3 releases a small amount of labelled data in the tourist information domain; participants will use this data plus the restaurant data from DSTC 2 for training. Dialogs used for training are fully labelled; user transcriptions, user dialog-act semantics and dialog state are all annotated. (This corpus therefore is also suitable for studies in Spoken Language Understanding.) Source: https://github.com/matthen/dstc Image Source: https://www.aclweb.org/anthology/W13-4065.pdf

To analyze how these capabilities would mesh together in a natural conversation, and compare the performance of different architectures and training schemes. Source: Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills

The IMAGE-CHAT dataset is a large collection of (image, style trait for speaker A, style trait for speaker B, dialogue between A & B) tuples that we collected using crowd-workers, Each dialogue consists of consecutive turns by speaker A and B. No particular constraints are placed on the kinds of utterance, only that we ask the speakers to both use the provided style trait, and to respond to the given image and dialogue history in an engaging way. The goal is not just to build a diagnostic dataset but a basis for training models that humans actually want to engage with.

A machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog. Molweni's source samples from the Ubuntu Chat Corpus, including 10,000 dialogs comprising 88,303 utterances. Source: Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure

The WoZ 2.0 dataset is a newer dialogue state tracking dataset whose evaluation is detached from the noisy output of speech recognition systems. Similar to DSTC2, it covers the restaurant search domain and has identical evaluation. Description from NLP Progress Image source: Mrkšić et al.

MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage. We aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents.

CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides. Source: CrossWOZ

Task Oriented Parsing v2 (TOPv2) representations for intent-slot based dialog systems. Provided under the CC-BY-SA license. Please cite the accompanying paper when using this dataset - @inproceedings{chen-etal-2020-low-resource, title={Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing}, author={Xilun Chen and Asish Ghoshal and Yashar Mehdad and Luke Zettlemoyer and Sonal Gupta}, booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year={2020}, publisher = "Association for Computational Linguistics" } CHANGELOG:<br> 03/10/2021 (V1.1): Added the low-resource splits used in the paper.<br> 09/18/2020 (V1.0): Initial release. TOPv2 is a multi-domain task-oriented semantic parsing dataset. It is an extension to the TOP dataset (http://fb.me/semanticparsingdialog) with 6 additional domains and 137k new samples. In total, TOPv2 has 8 domains (alarm, event, messaging, music, navigation, reminder, timer, weather) and 180k samples randomly split into train, eval, and test sets for each domain. Please refer to the paper for more data statistics. Note: As TOPv2 data is provided on a per-domain basis, the UNSUPPORTED utterances in the original TOP dataset were removed as they could not be mapped to any domain. The training, evaluation and test sets for each domain are provided as tab-separated value (TSV) files with file names of "domain_split.tsv". The first row of each file contains the column headers, while each following row is of the format: domain <tab> utterance <tab> semantic_parse where the semantic_parse follows the same format as the original TOP dataset. e.g. event <tab> Art fairs this weekend in Detroit <tab> [IN:GET_EVENT [SL:CATEGORY_EVENT Art fairs ] [SL:DATE_TIME this weekend ] in [SL:LOCATION Detroit ] ] The low-resource splits used in our experiments are provided in the low_resource_splits subdirectory, including training and validation sets from the reminder and weather domains under 10, 25, 50, 100, 250, 500 and 1000 SPIS.

In SpokenSQuAD, the document is in spoken form, the input question is in the form of text and the answer to each question is always a span in the document. The following procedures were used to generate spoken documents from the original SQuAD dataset. First, the Google text-to-speech system was used to generate the spoken version of the articles in SQuAD. Then CMU Sphinx was sued to generate the corresponding ASR transcriptions. The SQuAD training set was used to generate the training set of Spoken SQuAD, and SQuAD development set was used to generate the testing set for Spoken SQuAD. If the answer of a question did not exist in the ASR transcriptions of the associated article, the question-answer pair was removed from the dataset because these examples are too difficult for listening comprehension machine at this stage. Source: https://github.com/chiahsuan156/Spoken-SQuAD Image Source: https://github.com/chiahsuan156/Spoken-SQuAD

Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible to anyone without any restrictions. With ethical considerations in mind, we deliberately design annotation guidelines to obtain unambiguous annotations for all datasets. Furthermore, we build an evaluation system and carefully choose evaluations metrics for every task, thus establishing fair comparison across Korean language models. KLUE benchmark is composed of 8 tasks: - Topic Classification (TC) - Sentence Textual Similarity (STS) - Natural Language Inference (NLI) - Named Entity Recognition (NER) - Relation Extraction (RE) - (Part-Of-Speech) + Dependency Parsing (DP) - Machine Reading Comprehension (MRC) - Dialogue State Tracking (DST)

Taskmaster-1 is a dialog dataset consisting of 13,215 task-based dialogs in English, including 5,507 spoken and 7,708 written dialogs created with two distinct procedures. Each conversation falls into one of six domains: ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations. Image Source: https://arxiv.org/pdf/1909.05358v1.pdf

The MMD (MultiModal Dialogs) dataset is a dataset for multimodal domain-aware conversations. It consists of over 150K conversation sessions between shoppers and sales agents, annotated by a group of in-house annotators using a semi-automated manually intense iterative process. Source: Towards Building Large Scale Multimodal Domain-Aware Conversation Systems

xSID, a new evaluation benchmark for cross-lingual (X) Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect, covering Arabic (ar), Chinese (zh), Danish (da), Dutch (nl), English (en), German (de), Indonesian (id), Italian (it), Japanese (ja), Kazakh (kk), Serbian (sr), Turkish (tr) and an Austro-Bavarian German dialect, South Tyrolean (de-st).

DialoGLUE is a natural language understanding benchmark for task-oriented dialogue designed to encourage dialogue research in representation-based transfer, domain adaptation, and sample-efficient task learning. It consisting of 7 task-oriented dialogue datasets covering 4 distinct natural language understanding tasks. Source: DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue

Contains a base version (6.8million dialogues) and a large version (12.0 million dialogues). Source: A Large-Scale Chinese Short-Text Conversation Dataset

MMDialog is a large-scale multi-turn dialogue dataset containing multi-modal open-domain conversations derived from real human-human chat content in social media. MMDialog contains 1.08M dialogue sessions and 1.53M associated images. On average, one dialogue session has 2.59 images, which can be located anywhere at any conversation turn. Source: MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation Image Source: https://arxiv.org/pdf/2211.05719v1.pdf

SODA is a high-quality social dialogue dataset. In contrast to most existing crowdsourced, small-scale dialogue corpora, Soda distills 1.5M socially-grounded dialogues from a pre-trained language model (InstructGPT; Ouyang et al., ). Dialogues are distilled by contextualizing social commonsense knowledge from a knowledge graph (Atomic10x). Source: SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization Image Source: https://arxiv.org/pdf/2212.10465v1.pdf