ymoslem/acl-abstracts huggingface dataset:
Graph Neural Network Policies and Imitation Learning for Multi-Domain Task-Oriented Dialogues (2022)

Task-oriented dialogue systems are designed to achieve specific goals while conversing with humans. In practice, they may have to handle simultaneously several domains and tasks. The dialogue manager must therefore be able to take into account domain changes and plan over different domains/tasks in order to deal with multi-domain dialogues. However, learning with reinforcement in such context becomes difficult because the state-action dimension is larger while the reward signal remains scarce. Our experimental results suggest that structured policies based on graph neural networks combined with different degrees of imitation learning can effectively handle multi-domain dialogues. The reported experiments underline the benefit of structured policies over standard policies.
The DialPort tools (2022)

The DialPort project (\url{http://dialport.org/}), funded by the National Science Foundation (NSF), covers a group of tools and services that aim at fulfilling the needs of the dialog research community. Over the course of six years, several offerings have been created, including the DialPort Portal and DialCrowd. This paper describes these contributions, which will be demoed at SIGDIAL, including implementation, prior studies, corresponding discoveries, and the locations at which the tools will remain freely available to the community going forward.
Simultaneous Job Interview System Using Multiple Semi-autonomous Agents (2022)

In recent years, spoken dialogue systems have been applied to job interviews where an applicant talks to a system that asks pre-defined questions, called on-demand and self-paced job interviews. We propose a simultaneous job interview system, where one interviewer can conduct one-on-one interviews with multiple applicants simultaneously by cooperating with the multiple autonomous job interview dialogue systems. However, it is challenging for interviewers to monitor and understand all the parallel interviews done by the autonomous system at the same time. As a solution to this issue, we implemented two automatic dialogue understanding functions: (1) response evaluation of each applicant`s responses and (2) keyword extraction as a summary of the responses. It is expected that interviewers, as needed, can intervene in one dialogue and smoothly ask a proper question that elaborates the interview. We report a pilot experiment where an interviewer conducted simultaneous job interviews with three candidates.
Dialog Acts for Task Driven Embodied Agents (2022)

Embodied agents need to be able to interact in natural language {--} understanding task descriptions and asking appropriate follow up questions to obtain necessary information to be effective at successfully accomplishing tasks for a wide range of users. In this work, we propose a set of dialog acts for modelling such dialogs and annotate the TEACh dataset that includes over 3,000 situated, task oriented conversations (consisting of 39.5k utterances in total) with dialog acts. To our knowledge,TEACh-DA is the first large scale dataset of dialog act annotations for embodied task completion. Furthermore, we demonstrate the use of this annotated dataset in training models for tagging the dialog acts of a given utterance, predicting the dialog act of the next response given a dialog history, and use the dialog acts to guide agent`s non-dialog behaviour. In particular, our experiments on the TEACh Execution from Dialog History task where the model predicts the sequence of low level actions to be executed in the environment for embodied task completion, demonstrate that dialog acts can improve end performance by up to 2 points compared to the system without dialog acts.
Symbol and Communicative Grounding through Object Permanence with a Mobile Robot (2022)

Object permanence is the ability to form and recall mental representations of objects even when they are not in view. Despite being a crucial developmental step for children, object permanence has had only some exploration as it relates to symbol and communicative grounding in spoken dialogue systems. In this paper, we leverage SLAM as a module for tracking object permanence and use a robot platform to move around a scene where it discovers objects and learns how they are denoted. We evaluated by comparing our system`s effectiveness at learning words from human dialogue partners both with and without object permanence. We found that with object permanence, human dialogue partners spoke with the robot and the robot correctly identified objects it had learned about significantly more than without object permanence, which suggests that object permanence helped facilitate communicative and symbol grounding.
Towards Personality-Aware Chatbots (2022)

Chatbots are increasingly used to automate operational processes in customer service. However, most chatbots lack adaptation towards their users which may results in an unsatisfactory experience. Since knowing and meeting personal preferences is a key factor for enhancing usability in conversational agents, in this study we analyze an adaptive conversational agent that can automatically adjust according to a user`s personality type carefully excerpted from the Myers-Briggs type indicators. An experiment including 300 crowd workers examined how typifications like extroversion/introversion and thinking/feeling can be assessed and designed for a conversational agent in a job recommender domain. Our results validate the proposed design choices, and experiments on a user-matched personality typification, following the so-called law of attraction rule, show a significant positive influence on a range of selected usability criteria such as overall satisfaction, naturalness, promoter score, trust and appropriateness of the conversation.
Towards Socially Intelligent Agents with Mental State Transition and Human Value (2022)

Building a socially intelligent agent involves many challenges. One of which is to track the agent`s mental state transition and teach the agent to make decisions guided by its value like a human. Towards this end, we propose to incorporate mental state simulation and value modeling into dialogue agents. First, we build a hybrid mental state parser that extracts information from both the dialogue and event observations and maintains a graphical representation of the agent`s mind; Meanwhile, the transformer-based value model learns human preferences from the human value dataset, ValueNet. Empirical results show that the proposed model attains state-of-the-art performance on the dialogue/action/emotion prediction task in the fantasy text-adventure game dataset, LIGHT. We also show example cases to demonstrate: (i) how the proposed mental state parser can assist the agent`s decision by grounding on the context like locations and objects, and (ii) how the value model can help the agent make decisions based on its personal priorities.
Automatic Verbal Depiction of a Brick Assembly for a Robot Instructing Humans (2022)

Verbal and nonverbal communication skills are essential for human-robot interaction, in particular when the agents are involved in a shared task. We address the specific situation when the robot is the only agent knowing about the plan and the goal of the task and has to instruct the human partner. The case study is a brick assembly. We here describe a multi-layered verbal depictor whose semantic, syntactic and lexical settings have been collected and evaluated via crowdsourcing. One crowdsourced experiment involves a robot instructed pick-and-place task. We show that implicitly referring to achieved subgoals (stairs, pillows, etc) increases performance of human partners.
Are Interaction Patterns Helpful for Task-Agnostic Dementia Detection? An Empirical Exploration (2022)

Dementia often manifests in dialog through specific behaviors such as requesting clarification, communicating repetitive ideas, and stalling, prompting conversational partners to probe or otherwise attempt to elicit information. Dialog act (DA) sequences can have predictive power for dementia detection through their potential to capture these meaningful interaction patterns. However, most existing work in this space relies on content-dependent features, raising questions about their generalizability beyond small reference sets or across different cognitive tasks. In this paper, we adapt an existing DA annotation scheme for two different cognitive tasks present in a popular dementia detection dataset. We show that a DA tagging model leveraging neural sentence embeddings and other information from previous utterances and speaker tags achieves strong performance for both tasks. We also propose content-free interaction features and show that they yield high utility in distinguishing dementia and control subjects across different tasks. Our study provides a step toward better understanding how interaction patterns in spontaneous dialog affect cognitive modeling across different tasks, which carries implications for the design of non-invasive and low-cost cognitive health monitoring tools for use at scale.
EDU-AP: Elementary Discourse Unit based Argument Parser (2022)

Neural approaches to end-to-end argument mining (AM) are often formulated as dependency parsing (DP), which relies on token-level sequence labeling and intricate post-processing for extracting argumentative structures from text. Although such methods yield reasonable results, operating solely with tokens increases the possibility of discontinuous and overly segmented structures due to minor inconsistencies in token level predictions. In this paper, we propose EDU-AP, an end-to-end argument parser, that alleviates such problems in dependency-based methods by exploiting the intrinsic relationship between elementary discourse units (EDUs) and argumentative discourse units (ADUs) and operates at both token and EDU level granularity. Further, appropriately using contextual information, along with optimizing a novel objective function during training, EDU-AP achieves significant improvements across all four tasks of AM compared to existing dependency-based methods.
Using Transition Duration to Improve Turn-taking in Conversational Agents (2022)

Smooth turn-taking is an important aspect of natural conversation that allows interlocutors to maintain adequate mutual comprehensibility. In human communication, the timing between utterances is normatively constrained, and deviations convey socially relevant paralinguistic information. However, for spoken dialogue systems, smooth turn-taking continues to be a challenge. This motivates the need for spoken dialogue systems to employ a robust model of turn-taking to ensure that messages are exchanged smoothly and without transmitting unintended paralinguistic information. In this paper, we examine dialogue data from natural human interaction to develop an evidence-based model for turn-timing in spoken dialogue systems. First, we use timing between turns to develop two models of turn-taking: a speaker-agnostic model and a speaker-sensitive model. From the latter model, we derive the propensity of listeners to take the next turn given TRP duration. Finally, we outline how this measure may be incorporated into a spoken dialogue system to improve the naturalness of conversation.
DG2: Data Augmentation Through Document Grounded Dialogue Generation (2022)

Collecting data for training dialog systems can be extremely expensive due to the involvement of human participants and the need for extensive annotation. Especially in document-grounded dialog systems, human experts need to carefully read the unstructured documents to answer the users' questions. As a result, existing document-grounded dialog datasets are relatively small-scale and obstruct the effective training of dialogue systems. In this paper, we propose an automatic data augmentation technique grounded on documents through a generative dialogue model. The dialogue model consists of a user bot and agent bot that can synthesize diverse dialogues given an input document, which is then used to train a downstream model. When supplementing the original dataset, our method achieves significant improvement over traditional data augmentation methods. We also achieve great performance in the low-resource setting.
When can I Speak? Predicting initiation points for spoken dialogue agents (2022)

Current spoken dialogue systems initiate their turns after a long period of silence (700-1000ms), which leads to little real-time feedback, sluggish responses, and an overall stilted conversational flow. Humans typically respond within 200ms and successfully predicting initiation points in advance would allow spoken dialogue agents to do the same. In this work, we predict the lead-time to initiation using prosodic features from a pre-trained speech representation model (wav2vec 1.0) operating on user audio and word features from a pre-trained language model (GPT-2) operating on incremental transcriptions. To evaluate errors, we propose two metrics w.r.t. predicted and true lead times. We train and evaluate the models on the Switchboard Corpus and find that our method outperforms features from prior work on both metrics and vastly outperforms the common approach of waiting for 700ms of silence.
Using Interaction Style Dimensions to Characterize Spoken Dialog Corpora (2022)

The construction of spoken dialog systems today relies heavily on appropriate corpora, but corpus selection is more an art than a science. As interaction style properties govern many aspects of dialog, they have the potential to be useful for relating and comparing corpora. This paper overviews a recently-developed model of interaction styles and shows how it can be used to identify relevant corpus differences, estimate corpus similarity, and flag likely outlier dialogs.
Multi-Domain Dialogue State Tracking with Top-K Slot Self Attention (2022)

As an important component of task-oriented dialogue systems, dialogue state tracking is designed to track the dialogue state through the conversations between users and systems. Multi-domain dialogue state tracking is a challenging task, in which the correlation among different domains and slots needs to consider. Recently, slot self-attention is proposed to provide a data-driven manner to handle it. However, a full-support slot self-attention may involve redundant information interchange. In this paper, we propose a top-k attention-based slot self-attention for multi-domain dialogue state tracking. In the slot self-attention layers, we force each slot to involve information from the other k prominent slots and mask the rest out. The experimental results on two mainstream multi-domain task-oriented dialogue datasets, MultiWOZ 2.0 and MultiWOZ 2.4, present that our proposed approach is effective to improve the performance of multi-domain dialogue state tracking. We also find that the best result is obtained when each slot interchanges information with only a few slots.
Building a Knowledge-Based Dialogue System with Text Infilling (2022)

In recent years, generation-based dialogue systems using state-of-the-art (SoTA) transformer-based models have demonstrated impressive performance in simulating human-like conversations. To improve the coherence and knowledge utilization capabilities of dialogue systems, knowledge-based dialogue systems integrate retrieved graph knowledge into transformer-based models. However, knowledge-based dialog systems sometimes generate responses without using the retrieved knowledge. In this work, we propose a method in which the knowledge-based dialogue system can constantly utilize the retrieved knowledge using text infilling . Text infilling is the task of predicting missing spans of a sentence or paragraph. We utilize this text infilling to enable dialog systems to fill incomplete responses with the retrieved knowledge. Our proposed dialogue system has been proven to generate significantly more correct responses than baseline dialogue systems.
Generating Meaningful Topic Descriptions with Sentence Embeddings and LDA (2022)

A major part of business operations is interacting with customers. Traditionally this was done by human agents, face to face or over telephone calls within customer support centers. There is now a move towards automation in this field using chatbots and virtual assistants, as well as an increased focus on analyzing recorded conversations to gather insights. Determining the different services that a human agent provides and estimating the incurred call handling costs per service are key to prioritizing service automation. We propose a new technique, ELDA (Embedding based LDA), based on a combination of LDA topic modeling and sentence embeddings, that can take a dataset of customer-agent dialogs and extract key utterances instead of key words. The aim is to provide more meaningful and contextual topic descriptions required for interpreting and labeling the topics, reducing the need for manually reviewing dialog transcripts.
How Well Do You Know Your Audience? Toward Socially-aware Question Generation (2022)

When writing, a person may need to anticipate questions from their audience, but different social groups may ask very different types of questions. If someone is writing about a problem they want to resolve, what kind of follow-up question will a domain expert ask, and could the writer better address the expert`s information needs by rewriting their original post? In this paper, we explore the task of socially-aware question generation. We collect a data set of questions and posts from social media, including background information about the question-askers' social groups. We find that different social groups, such as experts and novices, consistently ask different types of questions. We train several text-generation models that incorporate social information, and we find that a discrete social-representation model outperforms the text-only model when different social groups ask highly different questions from one another. Our work provides a framework for developing text generation models that can help writers anticipate the information expectations of highly different social groups.
GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers (2022)

User simulators (USs) are commonly used to train task-oriented dialogue systems via reinforcement learning. The interactions often take place on semantic level for efficiency, but there is still a gap from semantic actions to natural language, which causes a mismatch between training and deployment environment. Incorporating a natural language generation (NLG) module with USs during training can partly deal with this problem. However, since the policy and NLG of USs are optimised separately, these simulated user utterances may not be natural enough in a given context. In this work, we propose a generative transformer-based user simulator (GenTUS). GenTUS consists of an encoder-decoder structure, which means it can optimise both the user policy and natural language generation jointly. GenTUS generates both semantic actions and natural language utterances, preserving interpretability and enhancing language variation. In addition, by representing the inputs and outputs as word sequences and by using a large pre-trained language model we can achieve generalisability in feature representation. We evaluate GenTUS with automatic metrics and human evaluation. Our results show that GenTUS generates more natural language and is able to transfer to an unseen ontology in a zero-shot fashion. In addition, its behaviour can be further shaped with reinforcement learning opening the door to training specialised user simulators.
AARGH! End-to-end Retrieval-Generation for Task-Oriented Dialog (2022)

We introduce AARGH, an end-to-end task-oriented dialog system combining retrieval and generative approaches in a single model, aiming at improving dialog management and lexical diversity of outputs. The model features a new response selection method based on an action-aware training objective and a simplified single-encoder retrieval architecture which allow us to build an end-to-end retrieval-enhanced generation model where retrieval and generation share most of the parameters. On the MultiWOZ dataset, we show that our approach produces more diverse outputs while maintaining or improving state tracking and context-to-response generation performance, compared to state-of-the-art baselines.
A Systematic Evaluation of Response Selection for Open Domain Dialogue (2022)

Recent progress on neural approaches for language processing has triggered a resurgence of interest on building intelligent open-domain chatbots. However, even the state-of-the-art neural chatbots cannot produce satisfying responses for every turn in a dialog. A practical solution is to generate multiple response candidates for the same context, and then perform response ranking/selection to determine which candidate is the best. Previous work in response selection typically trains response rankers using synthetic data that is formed from existing dialogs by using a ground truth response as the single appropriate response and constructing inappropriate responses via random selection or using adversarial methods. In this work, we curated a dataset where responses from multiple response generators produced for the same dialog context are manually annotated as appropriate (positive) and inappropriate (negative). We argue that such training data better matches the actual use case examples, enabling the models to learn to rank responses effectively. With this new dataset, we conduct a systematic evaluation of state-of-the-art methods for response selection, and demonstrate that both strategies of using multiple positive candidates and using manually verified hard negative candidates can bring in significant performance improvement in comparison to using the adversarial training data, e.g., increase of 3{\%} and 13{\%} in Recall@1 score, respectively.
Inferring Ranked Dialog Flows from Human-to-Human Conversations (2022)

We present a novel technique to infer ranked dialog flows from human-to-human conversations that can be used as an initial conversation design or to analyze the complexities of the conversations in a call center. This technique aims to identify, for a given service, the most common sequences of questions and responses from the human agent. Multiple dialog flows for different ranges of top paths can be produced so they can be reviewed in rank order and be refined in successive iterations until additional flows have the desired level of detail. The system ingests historical conversations and efficiently condenses them into a weighted deterministic finite-state automaton, which is then used to export dialog flow designs that can be readily used by conversational agents. A proof-of-concept experiment was conducted with the MultiWoz data set, a sample output is presented and future directions are outlined.