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.
651 PAPERS • 13 BENCHMARKS
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.
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A new open-vocabulary language modelling benchmark derived from books.
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DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 dialogues with corresponding manually labeled summaries and topics.
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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.
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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.
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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.
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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.
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This is a document grounded dataset for text conversations. "Document Grounded Conversations" are conversations that are about the contents of a specified document. In this dataset the specified documents are Wikipedia articles about popular movies. The dataset contains 4112 conversations with an average of 21.43 turns per conversation.
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We construct a dataset named CPED from 40 Chinese TV shows. CPED consists of multisource knowledge related to empathy and personal characteristic. This knowledge covers 13 emotions, gender, Big Five personality traits, 19 dialogue acts and other knowledge.
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Contains a base version (6.8million dialogues) and a large version (12.0 million dialogues).
FaithDial is a new benchmark for hallucination-free dialogues, by editing hallucinated responses in the Wizard of Wikipedia (WoW) benchmark.
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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).
Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them.
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PersonalDialog is a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker.
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KaMed is a knowledge-aware medical dialogue dataset, which contains over 60,000 medical dialogue sessions with 5,682 entities (such as Asthma and Atropine).
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OpenViDial is a large-scale open-domain dialogue dataset with visual contexts. The dialogue turns and visual contexts are extracted from movies and TV series, where each dialogue turn is paired with the corresponding visual context in which it takes place. OpenViDial contains a total number of 1.1 million dialogue turns, and thus 1.1 million visual contexts stored in images.
FusedChat is an inter-mode dialogue dataset. It contains dialogue sessions fusing task-oriented dialogues (TOD) and open-domain dialogues (ODD). Based on MultiWOZ, FusedChat appends or prepends an ODD to every existing TOD. See more details in the paper.
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OTTers is a dataset of human one-turn topic transitions. In this task, models must connect two topics in a cooperative and coherent manner, by generating a "bridging" utterance connecting the new topic tot he topic of the previous conversation turn.
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A large scale Chinese multi-modal dialogue corpus (120.84K dialogues and 198.82 K images). MMCHAT contains image-grounded dialogues collected from real conversations on social media. We manually annotate 100K dialogues from MMCHAT with the dialogue quality and whether the dialogues are related to the given image. We provide the rule-filtered raw dialogues that are used to create MMChat (Rule Filtered Raw MMChat). It contains 4.257 M dialogue sessions and 4.874 M images We provide a version of MMChat that is filtered based on LCCC (LCCC Filtered MMChat). This version contain much cleaner dialogues (492.6 K dialogue sessions and 1.066 M images)
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Collected by leveraging background knowledge from a larger, more highly represented dialogue source.
MDIA is a large-scale multilingual benchmark for dialogue generation. It covers real-life conversations in 46 languages across 19 language families.
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WDC-Dialogue is a dataset built from the Chinese social media to train EVA. Specifically, conversations from various sources are gathered and a rigorous data cleaning pipeline is designed to enforce the quality of WDC-Dialogue.
The high-quality multi-turn dialogue dataset, which has a total of 3,134 multi-turn consultation dialogues. CPsyCounD covers nine representative topics and seven classic schools of psychological counseling.
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carecall is a Korean dialogue dataset for role-satisfying dialogue systems. The dataset was composed with a few samples of human-written dialogues using in-context few-shot learning of large-scale LMs. Large-scale LMs can generate dialogues with a specific personality, given a prompt consisting of a brief description of the chatbot’s properties and few dialogue examples. We use this method to build the entire dataset.
The Arabic-TOD dataset is based on the BiToD dataset. Of the 3,689 BiToD-English dialogues, 1,500 dialogues (30,000 utterances) were translated into Arabic. We translated the task-related keywords such as cuisine, dietary restrictions, and price-level for the restaurant domain, price-level for the hotel domain, type, and price-level for the attraction domain, day, weather, and city for the weather domain. We keep the rest of values without translation, like hotels’ and restaurants’ names, locations, and addresses. These values are real entities in Hong Kong city (literals), and most of them contain Chinese words written in English, therefore they have not been translated. According to the slot-values in the Arabic-TOD dataset, we used the slots names as they are in English and translated their corresponding values, except the entities in Hong Kong city since the Arabic-TOD dataset supports codeswitching.
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The general multi-turn dialogue evaluation dataset with nine topics. Each topic has five representative cases, resulting in a comprehensive evaluation dataset of 45 cases.
Harry Potter Dialogue is the first dialogue dataset that integrates with scene, attributes and relations which are dynamically changed as the storyline goes on. Our work can facilitate research to construct more human-like conversational systems in practice. For example, virtual assistant, NPC in games, etc. Moreover, HPD can both support dialogue generation and retrieval tasks.
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JDDC 2.0 is a large-scale multimodal multi-turn dialogue dataset collected from a mainstream Chinese E-commerce platform JD.com, containing about 246 thousand dialogue sessions, 3 million utterances, and 507 thousand images, along with product knowledge bases and image category annotations. The dataset is divided into the training set, the validation set, and the test set according to the ratio of 80%, 10%, and 10%.
MultiRefKGC is a dataset created from conversations from Reddit designed for Knowledge-Grounded Dialogue Generation tasks.
OpenViDial 2.0 is a larger-scale open-domain multi-modal dialogue dataset compared to the previous version OpenViDial 1.0. OpenViDial 2.0 contains a total number of 5.6 million dialogue turns extracted from either movies or TV series from different resources, and each dialogue turn is paired with its corresponding visual context.
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The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents