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Dialogue 任务描述、数据资源和相关论文

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Dialogue

众所周知，对话难以评估。过去的方法使用了人类评估。

Dialogue act classification

Dialogue act classification is the task of classifying an utterance with respect to the fuction it serves in a dialogue, i.e. the act the speaker is performing. Dialogue acts are a type of speech acts (for Speech Act Theory, see Austin (1975) and Searle (1969)).

Switchboard corpus

The Switchboard-1 corpus is a telephone speech corpus, consisting of about 2,400 two-sided telephone conversation among 543 speakers with about 70 provided conversation topics. The dataset includes the audio files and the transcription files, as well as information about the speakers and the calls.

The Switchboard Dialogue Act Corpus (SwDA) [download] extends the Switchboard-1 corpus with tags from the SWBD-DAMSL tagset, which is an augmentation to the Discourse Annotation and Markup System of Labeling (DAMSL) tagset. The 220 tags were reduced to 42 tags by clustering in order to improve the language model on the Switchboard corpus. A subset of the Switchboard-1 corpus consisting of 1155 conversations was used. The resulting tags include dialogue acts like statement-non-opinion, acknowledge, statement-opinion, agree/accept, etc.
Annotated example:
Speaker: A, Dialogue Act: Yes-No-Question, Utterance: So do you go to college right now?

Model	Accuracy	Paper / Source	Code
CRF-ASN (Chen et al., 2018)	81.3	Dialogue Act Recognition via CRF-Attentive Structured Network
Bi-LSTM-CRF (Kumar et al., 2017)	79.2	Dialogue Act Sequence Labeling using Hierarchical encoder with CRF	Link
RNN with 3 utterances in context (Bothe et al., 2018)	77.34	A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks

ICSI Meeting Recorder Dialog Act (MRDA) corpus

The MRDA corpus [download] 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.
Annotated example:
Time: 2804-2810, Speaker: c6, Dialogue Act: s^bd, Transcript: i mean these are just discriminative.
Multiple dialogue acts are separated by “^”.

Model	Accuracy	Paper / Source	Code
CRF-ASN (Chen et al., 2018)	91.7	Dialogue Act Recognition via CRF-Attentive Structured Network
Bi-LSTM-CRF (Kumar et al., 2017)	90.9	Dialogue Act Sequence Labeling using Hierarchical encoder with CRF	Link

Dialogue state tracking

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.

Second dialogue state tracking challenge

For goal-oriented dialogue, the dataset of the second Dialogue Systems Technology Challenges
(DSTC2) is a common evaluation dataset. The DSTC2 focuses on the restaurant search domain. Models are
evaluated based on accuracy on both individual and joint slot tracking.

Model	Request	Area	Food	Price	Joint	Paper / Source
Zhong et al. (2018)	97.5	-	-	-	74.5	Global-locally Self-attentive Dialogue State Tracker
Liu et al. (2018)	-	90	84	92	72	Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems
Neural belief tracker (Mrkšić et al., 2017)	96.5	90	84	94	73.4	Neural Belief Tracker: Data-Driven Dialogue State Tracking
RNN (Henderson et al., 2014)	95.7	92	86	86	69	Robust dialog state tracking using delexicalised recurrent neural networks and unsupervised gate

Wizard-of-Oz

The WoZ 2.0 dataset is a newer dialogue state tracking dataset whose evaluation is detached from the noisy output of speech recognition systems. Similar to DSTC2, it covers the restaurant search domain and has identical evaluation.

Model	Request	Joint	Paper / Source
Zhong et al. (2018)	97.1	88.1	Global-locally Self-attentive Dialogue State Tracker
Neural belief tracker (Mrkšić et al., 2017)	96.5	84.4	Neural Belief Tracker: Data-Driven Dialogue State Tracking
RNN (Henderson et al., 2014)	87.1	70.8	Robust dialog state tracking using delexicalised recurrent neural networks and unsupervised gate