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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The Switchboard-1 Telephone Speech Corpus (LDC97S62) consists of approximately 260 hours of speech and was originally collected by Texas Instruments in 1990-1, under DARPA sponsorship. The first release of the corpus was published by NIST and distributed by the LDC in 1992-3.
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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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The MRDA corpus consists of about 75 hours of speech from 75 naturally-occurring meetings among 53 speakers. The tagset used for labeling is a modified version of the SWBD-DAMSL tagset. It is annotated with three types of information: marking of the dialogue act segment boundaries, marking of the dialogue acts and marking of correspondences between dialogue acts.
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Emotional Dialogue Acts data contains dialogue act labels for existing emotion multi-modal conversational datasets. We chose two popular multimodal emotion datasets: Multimodal EmotionLines Dataset (MELD) and Interactive Emotional dyadic MOtion CAPture database (IEMOCAP). EDAs reveal associations between dialogue acts and emotional states in a natural-conversational language such as Accept/Agree dialogue acts often occur with the Joy emotion, Apology with Sadness, and Thanking with Joy.
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Situated Dialogue Navigation (SDN) is a navigation benchmark of 183 trials with a total of 8415 utterances, around 18.7 hours of control streams, and 2.9 hours of trimmed audio. SDN is developed to evaluate the agent's ability to predict dialogue moves from humans as well as generate its own dialogue moves and physical navigation actions.
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