CAsT-19: A Dataset for Conversational Information Seeking
By Jeffrey Dalton, Chenyan Xiong, Vaibhav Kumar, Jamie Callan. 2020
80 citations recorded by the source corpus.
CAsT-19 is a new dataset that supports research on conversational information seeking. The corpus is 38,426,252 passages from the TREC Complex Answer Retrieval (CAR) and Microsoft MAchine Reading COmprehension (MARCO) datasets. Eighty information seeking dialogues (30 train, 50 test) are an average of 9 to 10 questions long. A dialogue may explore a topic broadly or drill down into subtopics. Questions contain ellipsis, implied context, mild topic shifts, and other characteristics of human conversation that may prevent them from being understood in isolation. Relevance assessments are provided for 30 training topics and 20 test topics. CAsT-19 promotes research on conversational information seeking by defining it as a task in which effective passage selection requires understanding a question's context (the dialogue history). It focuses attention on user modeling, analysis of prior retrieval results, transformation of questions into effective queries, and other topics that have been difficult to study with existing datasets.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jimmy J. Lin
- Nick Craswell
- Bhaskar Mitra
- Sebastian Hofstätter
- Emine Yilmaz
- Daniel Fernando Campos
- Jamie Callan
- Noah A. Smith
In Natural Language Processing
- Jimmy J. Lin
- Slav Petrov
- Dipanjan Das
- Bhaskar Mitra
- Nick Craswell
- Percy Liang
- Noah A. Smith
- Michael Collins
Citations in the corpus
- Measuring Attribution in Natural Language Generation Models (2021, methodology): “Set-up We use the QReCC dataset (Anantha et al., 2021), a collection of multi-turn conversational QA interactions that extends conversations coming from NaturalQuestions (Kwiatkowski et al., 2019), QUAC (Choi et al., 2018), and CAST-19 (Dalton et al., 2020).”
- CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning (2021, background): “Kumar and Callan (2020) propose a framework focusing on improving the passage re-ranker after the retrieval.”
- MS MARCO: Benchmarking Ranking Models in the Large-Data Regime (2021, methodology): “Another interesting case study in this context is the application of MS MARCO to conversational search where it has been useful for both creation of new benchmarks [17, 18, 59] and model training [26, 39, 50, 70, 77–79, 84, 86].”