Open-Domain Dialog

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/

KILT (Knowledge Intensive Language Tasks) is a benchmark consisting of 11 datasets representing 5 types of tasks: * Fact-checking (FEVER), * Entity linking (AIDA CoNLL-YAGO, WNED-WIKI, WNED-CWEB), * Slot filling (T-Rex, Zero Shot RE), * Open domain QA (Natural Questions, HotpotQA, TriviaQA, ELI5), * Dialog generation (Wizard of Wikipedia). All these datasets have been grounded in a single pre-processed wikipedia snapshot, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning. Source: KILT Benchmarking

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.

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

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.

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.

DuLeMon is a large-scale Chinese Long-term Memory Conversation dataset, which simulates long-term memory conversations and focuses on the ability to actively construct and utilize the user's and the bot's persona in a long-term interaction. DuLeMon contains about 27.5k human-human conversations, 449k utterances, and 12k persona grounding sentences. This corpus can be used to explore Long-term Memory Conversation, Personalized Dialogue, and Persona Extraction / Matching / Retrieval.

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

This dataset was collected with the goal of assessing dialog evaluation metrics. In the paper, USR: An Unsupervised and Reference Free Evaluation Metric for Dialog (Mehri and Eskenazi, 2020), the authors collect this data to measure the quality of several existing word-overlap and embedding-based metrics, as well as their newly proposed USR metric.

This dataset was collected with the goal of assessing dialog evaluation metrics. In the paper, USR: An Unsupervised and Reference Free Evaluation Metric for Dialog (Mehri and Eskenazi, 2020), the authors collect this data to measure the quality of several existing word-overlap and embedding-based metrics, as well as their newly proposed USR metric.

Reddit Conversation Corpus (RCC) consists of conversations, scraped from Reddit, for a 20 month period from November 2016 until August 2018. To ensure the quality and diversity of topics, 95 subreddits are selected from which conversations are collected. In total, RCC contains 9.2 million 3-turn conversations.

ClovaCall is a new large-scale Korean call-based speech corpus under a goal-oriented dialog scenario from more than 11,000 people. The raw dataset of ClovaCall includes approximately 112,000 pairs of a short sentence and its corresponding spoken utterance in a restaurant reservation domain. Source: https://github.com/ClovaAI/ClovaCall

• 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)

Diamante is a novel and efficient framework consisting of a data collection strategy and a learning method to boost the performance of pre-trained dialogue models. Two kinds of human feedback are collected and leveraged in Diamante, including explicit demonstration and implicit preference. The Diamante dataset is publicly available at the LUGE platform.

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...)

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

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.

Reddit Engagement Dataset (RED), a distant-supervision set, with 80k single-turn conversations. RED is sourced from Reddit, sampling from 43 popular subreddits, and processed from a total of 5 million posts, filtering out data that was either non-conversational, toxic, or posts not possible to ascertain popularity. Source: EnDex: Evaluation of Dialogue Engagingness at Scale Image Source: https://arxiv.org/pdf/2210.12362v1.pdf

The primary data of the SaGA corpus are made up of 25 dialogs of interlocutors (50), who engage in a spatial communication task combining direction-giving and sight description. Six of those dialogues with data only from the direction giver are available including audio (.wav) and video (.mp4) data. The secondary data consists of annotations (*.eaf) of gestures and speech-gesture referents, which have been completely and systematically annotated based on an annotation grid (cf. the SaGA documentation). The corpus is comprised of of 9881 isolated words and 1764 isolated gestures. The stimulus is a model of a town presented in a Virtual Reality (VR) environment. Upon finishing a "bus ride" through the VR town along five landmarks, a router explained the route as well as the wayside landmarks to an unknown and naive follower. The SaGA Corpus was curated for CLARIN as part of the Curation Project "Editing and Integration of Multimodal Resources in CLARIN-D" by the CLARIN-D Working Group 6 "Speech and Other Modalities".