Dialog: a window that appears on a screen in computing contexts (e.g., dialog box).

Application

• Dialogue System: a computer system that interacts with humans in natural language.

• Conversational Agent: a dialogue system that interprets and responds to user statements.

• Virtual Assistant: a dialogue system that performs tasks or services for user requests.

• Chatbot: a dialogue system that simulates and processes human conversation.

Intelligence

• Dialogue Management: a process of controlling the state and flow of the dialogue to conduct contextual communications.

• Conversational AI: a type of Artificial Intelligence (AI) for a dialogue system to understand user inputs and respond properly to them, often processed by machine learning models.

Properties

Unit

• Turn: a single contribution from one speaker to the dialogue.

• Utterance: a natural unit of speech bounded by breaths or pauses.

Context

• Speech Act: the action, either explicitly or implicitly, expressed by an utterance (e.g., answering, advising, greeting; see Switchboard Dialog Act Corpus).

• Intent: the user's goal expressed by an utterance within the context of a conversation (e.g., making an appointment, requesting information).

• Topic: the matter dealt with in an utterance (e.g., movie, family, midterm).

Scopes

Task-oriented

Task-oriented dialogue systems have specific tasks to be accomplished:

• The Second Dialog State Tracking Challenge, Henderson et al., SIGDIAL, 2014 (dataset).

• Conditional Generation and Snapshot Learning in Neural Dialogue Systems, Wen et al., EMNLP 2016 (dataset).

• Learning End-to-End Goal-Oriented Dialog, Bordes et al., ICLR, 2017 (dataset).

• , Eric et al., SIGDIAL, 2017 ().

• , Budzianowski et al., EMNLP, 2018 ().

• , Qin et al., EMNLP, 2019 ().

• , Rastogi et al., AAAI, 2020 ().

Open-domain

Open-domain dialogue systems aim to talk about any topics without specific end goals:

• Alexa Prize Socialbot Grand Challenge (Emora demo)

• Meta BlenderBot (demo)

• OpenAI ChatGPT (demo; requires login)

• (; )

Techniques

State Machine

A dialogue flow can be designed into a fine-state machine. Most commercial dialogue systems take this approach because it gives greater control over how the systems behave. Several platforms are available to facilitate the development of state machine-based dialogue systems:

• Amazon Lex

• Google Dialogflow

• IBM Watson Assistant

End-to-End

Recent researches focus on developing end-to-end dialogue systems using sequence-to-sequence (S2S) models, which is a type of encoder-decoder model:

• Sequence to Sequence Learning with Neural Networks, Sutskever et al., NeurIPS, 2014.

The current state-of-the-art S2S models use transformers such as BERT as their encoders:

• Attention is All you Need, Vaswani et al., NeurIPS, 2017.

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Devlin et al., NAACL, 2019.

Three of the open-domain dialogue systems above, Meta BlenderBot, OpenAI ChatGPT, and Google LaMDA, are end-to-end systems based on S2S models.

Assessments

The primary objective of both task-oriented and open-domain dialogue systems is to satisfy users by communicating with them. For task-oriented, users are generally satisfied if the tasks are accomplished efficiently. For open-domain, however, user satisfaction is often highly subjective, so proper conversational analysis may need to be involved.

• Towards Unified Dialogue System Evaluation: A Comprehensive Analysis of Current Evaluation Protocols, Finch and Choi, SIGDIAL, 2020.

• Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges, Mehri et al., arXiv, 2022.

• Don't Forget Your ABC's: Evaluating the State-of-the-Art in Chat-Oriented Dialogue Systems, Finch et al., arXiv, 2022.

You will meet three groups during this class. Each team discussion will last 20 minutes, including the time to find group members:

1. Find 3-4 people you have not interacted with from your previous group(s).

2. Discuss your project idea with your group and check if anyone has a similar interest.

Resource

• , Speech and Language Processing (3rd ed.), Jurafsky and Martin.