Dialogue Generation

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/

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.

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

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

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

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.

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.

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

FaithDial is a new benchmark for hallucination-free dialogues, by editing hallucinated responses in the Wizard of Wikipedia (WoW) benchmark. FaithDial contains around 50K turns across 5.5K conversations. If trained on FaithDial, state-of-the-art dialogue models are significantly more faithful while also enhancing other dialogue aspects like cooperativeness, creativity and engagement.

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. Source: https://github.com/festvox/datasets-CMU_DoG Image Source: https://arxiv.org/pdf/1809.07358v1.pdf

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. * We build a multiturn Chinese Personalized and Emotional Dialogue dataset called CPED. To the best of our knowledge, CPED is the first Chinese personalized and emotional dialogue dataset. CPED contains 12K dialogues and 133K utterances with multi-modal context. Therefore, it can be used in both complicated dialogue understanding and human-like conversation generation. * CPED has been annotated with 3 character attributes (name, gender age), Big Five personality traits, 2 types of dynamic emotional information (sentiment and emotion) and DAs. The personality traits and emotions can be used as prior external knowledge for open-domain conversation generation, making the conversation system have a good command of personification capabilities. * We propose three tasks for CPED: personality recognition in conversations (PRC), emotion recognition in conversations (ERC), and personalized and emotional conversation (PEC). A set of experiments verify the importance of using personalities and emotions as prior external knowledge for conversation generation.

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. Source: Personalized Dialogue Generation with Diversified Traits Image Source: https://arxiv.org/pdf/1901.09672v2.pdf

Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale multi-turn dialogue dataset to teach conversational agents to respond to problematic content following social norms. Covering diverse unethical, problematic, biased, and toxic situations, ProsocialDialog contains responses that encourage prosocial behavior, grounded in commonsense social rules (i.e., rules-of-thumb, RoTs). ProsocialDialog consists of 58K dialogues between a speaker showing potentially unsafe behavior and a speaker giving constructive feedback for more socially acceptable behavior. Specifically, it contains a rich suite of: * 331K utterances * 160K Rules-of-thumb (RoTs) * 497K dialogue safety labels accompanied by free-form rationales

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).

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. Source: https://github.com/ShannonAI/OpenViDial Image Source: https://github.com/ShannonAI/OpenViDial

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.

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.

• 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)

Collected by leveraging background knowledge from a larger, more highly represented dialogue source. Source: Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach

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. #### Topic types * Self-growth * Emotion&Stress * Education * Love&Marriage * Family Relationship * Social Relationship * Sex * Career * Mental Disease #### Consulting schools * Psychoanalytic Therapy * Cognitive Behavioral Therapy * Humanistic Therapy * Family Therapy * Postmodern Therapy * Integrative Therapy * Other Therapies (Mindfulness/Morita therapy...)

MDIA is a large-scale multilingual benchmark for dialogue generation. It covers real-life conversations in 46 languages across 19 language families.

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 dataset mainly focuses on three categories of textual interaction data, i.e., repost on social media, comment / reply on various online forums and online question and answer (Q&A) exchanges. Each round of these textual interactions yields a dialogue session via well-designed parsing rules.

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 general multi-turn dialogue evaluation dataset with nine topics. Each topic has five representative cases, resulting in a comprehensive evaluation dataset of 45 cases. ### Topic types * Self-growth * Emotion&Stress * Education * Love&Marriage * Family Relationship * Social Relationship * Sex * Career * Mental Disease

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. Image source: https://github.com/ShannonAI/OpenViDial

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. We did not translate the 'UserTask' for all dialogues, since it is not important in developing the system. It is just as a summarization of the dialogue contents.

Dataset for our paper Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky which includes 5000 enterprise tools and the corresponding dialogues generated using DiaFORGE UTC data engine. The dataset is generated with the data generation engine. The engine simulates a user agent and an assistant agent in a dialogue, where the user agent has a persona and the assistant agent has access to a set of tools. Detailed information about the data generation process can be found in the paper. Each entry in the dataset contains: - seed: Gold tool for the generated dialogue. - user_persona: Persona of the simulated user agent. - messages: List of messages in the dialogue, where each message is a dictionary containing: - distractor_tools: List of distractor tools that are not the gold tool but are relevant to the dialogue. These tools are used by user agent to generate utterances that are hard to disambiguate from the gold tool. - retrieved_tools: List of tools retrieved by the assistant agent and used by the assistant agent in order to ask clarifying questions to the user agent.

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.

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%.

To evaluate our proposed strategy of asynchronous communication for LLMs, we run games of Mafia with human players, incorporating an LLM-based agent as an additional player, within an asynchronous chat environment. The data includes all players messages and votes including timestamps, game management messages (e.g., announcements of the beginning and end of phases), in addition to records related to the agent, such as the prompts that were provided at each timestamp. While prior datasets for Mafia exist, ours is the first to integrate an LLM agent, allowing analysis of human-LLM interactions. Detailed analysis of the data is available in our paper: Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games

MultiRefKGC is a dataset created from conversations from Reddit designed for Knowledge-Grounded Dialogue Generation tasks. Source: There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning Image Source: https://arxiv.org/pdf/2210.12459v1.pdf

The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents > > Xing Han Lu, Siva Reddy, Harm de Vries > > EACL 2023 | | | | | | | :--: | :--: | :--: | :--: | :--: | | Code | Huggingface | Request on Dataverse | Paper | Website | Banner