태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Open intent discovery aims to leverage limited prior knowledge of known intents to find fine-grained known and open intent-wise clusters.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
DBpedia (from "DB" for "database") is a project aiming to extract structured content from the information created in the Wikipedia project. DBpedia allows users to semantically query relationships and properties of Wikipedia resources, including links to other related datasets. Source: https://en.wikipedia.org/wiki/DBpedia
The ATIS (Airline Travel Information Systems) is a dataset consisting of audio recordings and corresponding manual transcripts about humans asking for flight information on automated airline travel inquiry systems. The data consists of 17 unique intent categories. The original split contains 4478, 500 and 893 intent-labeled reference utterances in train, development and test set respectively. Source: Spoken Language Intent Detection using Confusion2Vec
The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity: * SearchCreativeWork (e.g. Find me the I, Robot television show), * GetWeather (e.g. Is it windy in Boston, MA right now?), * BookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night), * PlayMusic (e.g. Play the last track from Beyoncé off Spotify), * AddToPlaylist (e.g. Add Diamonds to my roadtrip playlist), * RateBook (e.g. Give 6 stars to Of Mice and Men), * SearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris). The training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent. Source: https://paperswithcode.com/paper/snips-voice-platform-an-embedded-spoken/
Dataset composed of online banking queries annotated with their corresponding intents. BANKING77 dataset provides a very fine-grained set of intents in a banking domain. It comprises 13,083 customer service queries labeled with 77 intents. It focuses on fine-grained single-domain intent detection.
This dataset is for evaluating the performance of intent classification systems in the presence of "out-of-scope" queries, i.e., queries that do not fall into any of the system-supported intent classes. The dataset includes both in-scope and out-of-scope data. Source: CLINC150
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.