ymoslem/acl-abstracts huggingface dataset:
Dialogue Discourse Parsing as Generation: A Sequence-to-Sequence LLM-based Approach (2024)

Existing works on dialogue discourse parsing mostly utilize encoder-only models and sophisticated decoding strategies to extract structures. Despite recent advances in Large Language Models (LLMs), there has been little work applying directly these models on discourse parsing. To fully utilize the rich semantic and discourse knowledge in LLMs, we explore the feasibility of transforming discourse parsing into a generation task using a text-to-text paradigm. Our approach is intuitive and requires no modification of the LLM architecture. Experimental results on STAC and Molweni datasets show that a sequence-to-sequence model such as T0 can perform reasonably well. Notably, our improved transition-based sequence-to-sequence system achieves new state-of-the-art performance on Molweni, demonstrating the effectiveness of the proposed method. Furthermore, our systems can generate richer discourse structures such as directed acyclic graphs, whereas previous methods are limited to trees.
Rhetorical Strategies in the UN Security Council: Rhetorical Structure Theory and Conflicts (2024)

More and more corpora are being annotated with Rhetorical Structure Theory (RST) trees, often in a multi-layer scenario, as analyzing RST annotations in combination with other layers can lead to a deeper understanding of texts. To date, prior work on RST for the analysis of diplomatic language however, is scarce. We are interested in political speeches and investigate what rhetorical strategies diplomats use to communicate critique or deal with disputes. To this end, we present a new dataset with RST annotations of 82 diplomatic speeches aligned to existing Conflict annotations (UNSC-RST). We explore ways of using rhetorical trees to analyze an annotated multi-layer corpus, looking at both the relation distribution and the tree structure of speeches. In preliminary analyses we already see patterns that are characteristic for particular topics or countries.
Elaborative Simplification for German-Language Texts (2024)

There are many strategies used to simplify texts. In this paper, we focus specifically on the act of inserting information or elaborative simplification. Adding information is done for various reasons, such as providing definitions for concepts, making relations between concepts more explicit, and providing background information that is a prerequisite for the main content. As all of these reasons have the main goal of ensuring coherence, we first conduct a corpus analysis of simplified German-language texts that have been annotated with Rhetorical Structure Theory (RST). We focus specifically on how additional information is incorporated into the RST annotation for a text. We then transfer these insights to automatic simplification using Large Language Models (LLMs), as elaborative simplification is a nuanced task which LLMs still seem to struggle with.
Examining Gender and Power on Wikipedia through Face and Politeness (2024)

We propose a framework for analyzing discourse by combining two interdependent concepts from sociolinguistic theory: face acts and politeness. While politeness has robust existing tools and data, face acts are less resourced. We introduce a new corpus created by annotating Wikipedia talk pages with face acts and we use this to train a face act tagger. We then employ our framework to study how face and politeness interact with gender and power in discussions between Wikipedia editors. Among other findings, we observe that female Wikipedians are not only more polite, which is consistent with prior studies, but that this difference corresponds with significantly more language directed at humbling aspects of their own face. Interestingly, the distinction nearly vanishes once limiting to editors with administrative power.
ReALM: Reference Resolution as Language Modeling (2024)

Reference resolution is an important problem, one that is essential to understand and successfully handle contexts of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user`s screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized. This paper demonstrates how LLMs can be used to create an effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality. We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5{\%} for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it.
Dialog Flow Induction for Constrainable LLM-Based Chatbots (2024)

LLM-driven dialog systems are used in a diverse set of applications, ranging from healthcare to customer service. However, given their generalization capability, it is difficult to ensure that these chatbots stay within the boundaries of the specialized domains, potentially resulting in inaccurate information and irrelevant responses. This paper introduces an unsupervised approach for automatically inducing domain-specific dialog flows that can be used to constrain LLM-based chatbots. We introduce two variants of dialog flow based on the availability of in-domain conversation instances. Through human and automatic evaluation over 24 dialog domains, we demonstrate that our high-quality data-guided dialog flows achieve better domain coverage, thereby overcoming the need for extensive manual crafting of such flows.
Knowledge-Grounded Dialogue Act Transfer using Prompt-Based Learning for Controllable Open-Domain NLG (2024)

Open domain spoken dialogue systems need to controllably generate many different dialogue acts (DAs) to allow Natural Language Generation (NLG) to create interesting and engaging conversational interactions with users. We aim to create an NLG engine that can produce a variety of DAs that make substantive knowledge-grounded contributions to a conversation. Training such an NLG typically requires dialogue corpora that are labelled for DAs, which are expensive to produce and vulnerable to quality issues. Here, we present a prompt-based learning approach to transfer DAs from one domain, video games, to 7 new domains. For each novel domain, we first crawl WikiData to create Meaning Representations that systematically vary both the number of attributes and hops on the WikiData Knowledge Graph. The proposed method involves a self-training step to create prompt examples for each domain followed by an overgeneration and ranking step. The result is a novel, high-quality dataset, Wiki-Dialogue, of 71K knowledge-grounded utterances, covering 9 DAs and the Art, Movies, Music, Sports, TV, Animal, and Boardgames domains, whose combined DA and semantic accuracy is 89{\%}. We assess the corpus quality using both automatic and human evaluations and find it high. The corpus is found to be safe, lexically rich, and large in vocabulary, when compared to similar datasets.
Incremental Learning for Knowledge-Grounded Dialogue Systems in Industrial Scenarios (2024)

In today`s industrial landscape, seamless collaboration between humans and machines is essential and requires a shared knowledge of the operational domain. In this framework, the technical knowledge for operator assistance has traditionally been derived from static sources such as technical documents. However, experienced operators hold invaluable know-how that can significantly contribute to support other operators. This work focuses on enhancing the operator assistance tasks in the manufacturing industry by leveraging spoken natural language interaction. More specifically, a Human-in-the-Loop (HIL) incremental learning approach is proposed to integrate this expertise into a domain knowledge graph (KG) dynamically, along with the use of in-context learning for Large Language Models (LLMs) to benefit other capabilities of the system. Preliminary results of the experimentation carried out in an industrial scenario, where the graph size was increased in a 25{\%}, demonstrate that the incremental enhancing of the KG benefits the dialogue system`s performance.
Anticipating Follow-Up Questions in Exploratory Information Search (2024)

The paper describes methods for anticipating follow-up questions in exploratory information search. There are two main cases: information stored in knowledge graphs, and information in unstructured texts such as Wikipedia. In the first case, follow-up questions are anticipated by extracting subgraphs relevant to user queries, passing the subgraphs to an LLM to generate responses. In the second case, entities and their relationships are extracted from the texts and added to short-term knowledge graphs relevant to initial queries. Follow-up questions are then anticipated by extracting subgraphs relevant to subsequent queries and passing the subgraphs to the LLM, as in the first case. The short-term graphs in dialogue memory are often sufficient to answer follow-up questions. If they are not, the described steps are repeated as required.
Bridging Information Gaps in Dialogues with Grounded Exchanges Using Knowledge Graphs (2024)

Knowledge models are fundamental to dialogue systems for enabling conversational interactions, which require handling domain-specific knowledge. Ensuring effective communication in information-providing conversations entails aligning user understanding with the knowledge available to the system. However, dialogue systems often face challenges arising from semantic inconsistencies in how information is expressed in natural language compared to how it is represented within the system`s internal knowledge. To address this problem, we study the potential of large language models for conversational grounding, a mechanism to bridge information gaps by establishing shared knowledge between dialogue participants. Our approach involves annotating human conversations across five knowledge domains to create a new dialogue corpus called BridgeKG. Through a series of experiments on this dataset, we empirically evaluate the capabilities of large language models in classifying grounding acts and identifying grounded information items within a knowledge graph structure. Our findings offer insights into how these models use in-context learning for conversational grounding tasks and common prediction errors, which we illustrate with examples from challenging dialogues. We discuss how the models handle knowledge graphs as a semantic layer between unstructured dialogue utterances and structured information items.
\textquotedblleftKeep up the good work!\textquotedblright: Using Constraints in Zero Shot Prompting to Generate Supportive Teacher Responses (2024)

Educational dialogue systems have been used to support students and teachers for decades. Such systems rely on explicit pedagogically motivated dialogue rules. With the ease of integrating large language models (LLMs) into dialogue systems, applications have been arising that directly use model responses without the use of human-written rules, raising concerns about their use in classroom settings. Here, we explore how to constrain LLM outputs to generate appropriate and supportive teacher-like responses. We present results comparing the effectiveness of different constraint variations in a zero-shot prompting setting on a large mathematics classroom corpus. Generated outputs are evaluated with human annotation for Fluency, Relevance, Helpfulness, and Adherence to the provided constraints. Including all constraints in the prompt led to the highest values for Fluency and Helpfulness, and the second highest value for Relevance. The annotation results also demonstrate that the prompts that result in the highest adherence to constraints do not necessarily indicate higher perceived scores for Fluency, Relevance, or Helpfulness. In a direct comparison, all of the non-baseline LLM responses were ranked higher than the actual teacher responses in the corpus over 50{\%} of the time.
HelloThere: A Corpus of Annotated Dialogues and Knowledge Bases of Time-Offset Avatars (2024)

A Time-Offset Interaction Application (TOIA) is a software system that allows people to engage in face-to-face dialogue with previously recorded videos of other people. There are two TOIA usage modes: (a) creation mode, where users pre-record video snippets of themselves representing their answers to possible questions someone may ask them, and (b) interaction mode, where other users of the system can choose to interact with created avatars. This paper presents the HelloThere corpus that has been collected from two user studies involving several people who recorded avatars and many more who engaged in dialogues with them. The interactions with avatars are annotated by people asking them questions through three modes (card selection, text search, and voice input) and rating the appropriateness of their answers on a 1 to 5 scale. The corpus, made available to the research community, comprises 26 avatars' knowledge bases and 317 dialogues between 64 interrogators and the avatars in text format.
It Couldn`t Help but Overhear: On the Limits of Modelling Meta-Communicative Grounding Acts with Supervised Learning (2024)

Active participation in a conversation is key to building common ground, since understanding is jointly tailored by producers and recipients. Overhearers are deprived of the privilege of performing grounding acts and can only conjecture about intended meanings. Still, data generation and annotation, modelling, training and evaluation of NLP dialogue models place reliance on the overhearing paradigm. How much of the underlying grounding processes are thereby forfeited? As we show, there is evidence pointing to the impossibility of properly modelling human meta-communicative acts with data-driven learning models. In this paper, we discuss this issue and provide a preliminary analysis on the variability of human decisions for requesting clarification. Most importantly, we wish to bring this topic back to the community`s table, encouraging discussion on the consequences of having models designed to only {\textquotedblleft}listen in'{\textquotedblright}.
Data Augmentation Integrating Dialogue Flow and Style to Adapt Spoken Dialogue Systems to Low-Resource User Groups (2024)

This study addresses the interaction challenges encountered by spoken dialogue systems (SDSs) when engaging with users who exhibit distinct conversational behaviors, particularly minors, in scenarios where data are scarce. We propose a novel data augmentation framework to enhance SDS performance for user groups with limited resources. Our approach leverages a large language model (LLM) to extract speaker styles and a pre-trained language model (PLM) to simulate dialogue act history. This method generates enriched and personalized dialogue data, facilitating improved interactions with unique user demographics. Extensive experiments validate the efficacy of our methodology, highlighting its potential to foster the development of more adaptive and inclusive dialogue systems.
StyEmp: Stylizing Empathetic Response Generation via Multi-Grained Prefix Encoder and Personality Reinforcement (2024)

Recent approaches for empathetic response generation mainly focus on emotional resonance and user understanding, without considering the system`s personality. Consistent personality is evident in real human expression and is important for creating trustworthy systems. To address this problem, we propose StyEmp, which aims to stylize the empathetic response generation with a consistent personality. Specifically, it incorporates a multi-grained prefix mechanism designed to capture the intricate relationship between a system`s personality and its empathetic expressions. Furthermore, we introduce a personality reinforcement module that leverages contrastive learning to calibrate the generation model, ensuring that responses are both empathetic and reflective of a distinct personality. Automatic and human evaluations on the EMPATHETICDIALOGUES benchmark show that StyEmp outperforms competitive baselines in terms of both empathy and personality expressions. Our code is available at https://github.com/fuyahuii/StyEmp.
Multi-Criteria Evaluation Framework of Selecting Response-worthy Chats in Live Streaming (2024)

Live streaming, a dynamic medium that merges real-time audiovisual content with interactive text-based chat, presents unique challenges for maintaining viewer engagement and ensuring streamers' well-being. This study introduces a multi-criteria evaluation framework designed to identify response-worthy chats during live streaming. We proposed a system that evaluates chats based on sentiment polarity and intensity, contextual relevance, and topic uniqueness. We also constructed a dataset annotated by human reviewers who validates the framework, demonstrating a closer alignment with human preferences compared to single-criterion baselines. This framework not only supports the development of more responsive and engaging live streaming environments but also contributes to the broader field of dialog systems by highlighting the distinct needs of real-time, large-scale conversational contexts.
Generating Unexpected yet Relevant User Dialog Acts (2024)

The demand for mental health services has risen substantially in recent years, leading to challenges in meeting patient needs promptly. Virtual agents capable of emulating motivational interviews (MI) have emerged as a potential solution to address this issue, offering immediate support that is especially beneficial for therapy modalities requiring multiple sessions. However, developing effective patient simulation methods for training MI dialog systems poses challenges, particularly in generating syntactically and contextually correct, and diversified dialog acts while respecting existing patterns and trends in therapy data. This paper investigates data-driven approaches to simulate patients for training MI dialog systems. We propose a novel method that leverages time series models to generate diverse and contextually appropriate patient dialog acts, which are then transformed into utterances by a conditioned large language model. Additionally, we introduce evaluation measures tailored to assess the quality and coherence of simulated patient dialog. Our findings highlight the effectiveness of dialog act-conditioned approaches in improving patient simulation for MI, offering insights for developing virtual agents to support mental health therapy.
Training LLMs to Recognize Hedges in Dialogues about Roadrunner Cartoons (2024)

Hedges allow speakers to mark utterances as provisional, whether to signal non-prototypicality or {\textquotedblleft}fuzziness{\textquotedblright}, to indicate a lack of commitment to an utterance, to attribute responsibility for a statement to someone else, to invite input from a partner, or to soften critical feedback in the service of face management needs. Here we focus on hedges in an experimentally parameterized corpus of 63 Roadrunner cartoon narratives spontaneously produced from memory by 21 speakers for co-present addressees, transcribed to text (Galati and Brennan, 2010). We created a gold standard of hedges annotated by human coders (the Roadrunner-Hedge corpus) and compared three LLM-based approaches for hedge detection: fine-tuning BERT, and zero and few-shot prompting with GPT-4o and LLaMA-3. The best-performing approach was a fine-tuned BERT model, followed by few-shot GPT-4o. After an error analysis on the top performing approaches, we used an LLM-in-the-Loop approach to improve the gold standard coding, as well as to highlight cases in which hedges are ambiguous in linguistically interesting ways that will guide future research. This is the first step in our research program to train LLMs to interpret and generate collateral signals appropriately and meaningfully in conversation.
On the Controllability of Large Language Models for Dialogue Interaction (2024)

This paper investigates the enhancement of Dialogue Systems by integrating the creative capabilities of Large Language Models. While traditional Dialogue Systems focus on understanding user input and selecting appropriate system actions, Language Models excel at generating natural language text based on prompts. Therefore, we propose to improve controllability and coherence of interactions by guiding a Language Model with control signals that enable explicit control over the system behaviour. To address this, we tested and evaluated our concept in 815 conversations with over 3600 dialogue exchanges on a dataset. Our experiment examined the quality of generated system responses using two strategies: An unguided strategy where task data was provided to the models, and a controlled strategy in which a simulated Dialogue Controller provided appropriate system actions. The results show that the average BLEU score and the classification of dialogue acts improved in the controlled Natural Language Generation.
Divide and Conquer: Rethinking Ambiguous Candidate Identification in Multimodal Dialogues with Pseudo-Labelling (2024)

Ambiguous Candidate Identification(ACI) in multimodal dialogue is the task of identifying all potential objects that a user`s utterance could be referring to in a visual scene, in cases where the reference cannot be uniquely determined. End-to-end models are the dominant approach for this task, but have limited real-world applicability due to unrealistic inference-time assumptions such as requiring predefined catalogues of items. Focusing on a more generalized and realistic ACI setup, we demonstrate that a modular approach, which first emphasizes language-only reasoning over dialogue context before performing vision-language fusion, significantly outperforms end-to-end trained baselines. To mitigate the lack of annotations for training the language-only module (student), we propose a pseudo-labelling strategy with a prompted Large Language Model (LLM) as the teacher.
Self-Emotion Blended Dialogue Generation in Social Simulation Agents (2024)

When engaging in conversations, dialogue agents in a virtual simulation environment may exhibit their own emotional states that are unrelated to the immediate conversational context, a phenomenon known as self-emotion. This study explores how such self-emotion affects the agents' behaviors in dialogue strategies and decision-making within a large language model (LLM)-driven simulation framework. In a dialogue strategy prediction experiment, we analyze the dialogue strategy choices employed by agents both with and without self-emotion, comparing them to those of humans. The results show that incorporating self-emotion helps agents exhibit more human-like dialogue strategies. In an independent experiment comparing the performance of models fine-tuned on GPT-4 generated dialogue datasets, we demonstrate that self-emotion can lead to better overall naturalness and humanness. Finally, in a virtual simulation environment where agents have free discussions, we show that self-emotion of agents can significantly influence the decision-making process of the agents, leading to approximately a 50{\%} change in decisions.