Papers with Code 아카이브

Dialogue State Tracking

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크
0
평가표
7
보존된 평가 기록
39

Dialogue state tacking consists of determining at each turn of a dialogue the full representation of what the user wants at that point in the dialogue, which contains a goal constraint, a set of requested slots, and the user's dialogue act.

기법

관련 논문에서 소개한 기법입니다. 이 태스크에서의 평가 여부는 평가 결과에서 확인하세요.

FastSGT

Fast Schema Guided Tracker, or FastSGT, is a fast and robust BERT-based model for state tracking in goal-oriented dialogue systems. The model employs carry-over mechanisms for transferring the values between slots, enabling switching between services and accepting the values offered by the system during dialogue. It also uses multi-head attention projections in some of the decoders to have a better modelling of the encoder outputs. The model architecture is illustrated in the Figure. It consists of four main modules: 1-Utterance Encoder, 2-Schema Encoder, 3-State Decoder, and 4-State Tracker. The first three modules constitute the NLU component and are based on neural networks, whereas the state tracker is a rule-based module. BERT was used for both encoders in the model. The Utterance Encoder is a BERT model which encodes the user and system utterances at each turn. The Schema Encoder is also a BERT model which encodes the schema descriptions of intents, slots, and values into schema embeddings. These schema embeddings help the decoders to transfer or share knowledge between different services by having some language understanding of each slot, intent, or value. The schema and utterance embeddings are passed to the State Decoder - a multi-task module. This module consists of five sub-modules producing the information necessary to track the state of the dialogue. Finally, the State Tracker module takes the previous state along with the current outputs of the State Decoder and predicts the current state of the dialogue by aggregating and summarizing the information across turns.

A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset

TransferQA

TransferQA is a transferable generative QA model, built upon T5 that combines extractive QA and multi-choice QA via a text-to-text transformer framework, and tracks both categorical slots and non-categorical slots in DST. In addition, it introduces two effective ways to construct unanswerable questions, namely, negative question sampling and context truncation, which enable the model to handle “none” value slots in the zero-shot DST setting.

Zero-Shot Dialogue State Tracking via Cross-Task Transfer

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