An official website of the United States government
While neural approaches to argument mining (AM) have advanced considerably, most of the recent work has been limited to parsing monologues. With an urgent interest in the use of conversational agents for broader societal applications, there is a need to advance the state-of-the-art in argument parsers for dialogues. This enables progress towards more purposeful conversations involving persuasion, debate and deliberation. This paper discusses Dialo-AP, an end-to-end argument parser that constructs argument graphs from dialogues. We formulate AM as dependency parsing of elementary and argumentative discourse units; the system is trained using extensive pre-training and curriculum learning comprising nine diverse corpora. Dialo-AP is capable of generating argument graphs from dialogues by performing all sub-tasks of AM. Compared to existing state-of-the-art baselines, Dialo-AP achieves significant improvements across all tasks, which is further validated through rigorous human evaluation.
more »
« less
Turiya at DialAM-2024: Inference Anchoring Theory Based LLM Parsers
Representing discourse as argument graphs facilitates robust analysis. Although computational frameworks for constructing graphs from monologues exist, there is a lack of frameworks for parsing dialogue. Inference Anchoring Theory (IAT) is a theoretical framework for extracting graphical argument structures and relationships from dialogues. Here, we introduce computational models for implementing the IAT framework for parsing dialogues. We experiment with a classification-based biaffine parser and Large Language Model (LLM)-based generative methods and compare them. Our results demonstrate the utility of finetuning LLMs for constructing IAT-based argument graphs from dialogues, which is a nuanced task.
more »
« less
- Award ID(s):
- 2214070
- PAR ID:
- 10543970
- Publisher / Repository:
- ACL Anthology
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Aydeniz, Mehmet (Ed.)Argumentation is a practice that spans STEM disciplines and is an explicit goal for K12 students in reform-based standards documents. The purpose of this study was to investigate the applicability of Douglas Walton’s theoretical model for describing the types of argument dialogue encountered in elementary classrooms focused on learning concepts in science, mathematics, and computer coding. We examined two elementary teachers’ STEM classrooms to explore the types of argument dialogue that were evident. We found evidence of six types of dialogues: persuasion, negotiation, information-seeking, deliberation, inquiry, and discovery based on Walton’s model. Our findings demonstrate the applicability of Walton’s types of argument dialogue to argumentation in elementary STEM contexts. Even though our work takes place in the United States with teachers of children in grades 3-5 (ages 8-10 years), we believe our approach is applicable to other dialogues found in K12 STEM education. We postulate that students having opportunities to engage in arguments with a diverse range of goals (e.g., to prove a hypothesis, to persuade, or to exchange information) is important for their development in learning how to argue in STEM.more » « less
-
Video-language models (VLMs), large models pre-trained on numerous but noisy video-text pairs from the internet, have revolutionized activity recognition through their remarkable generalization and open-vocabulary capabilities. While complex human activities are often hierarchical and compositional, most existing tasks for evaluating VLMs focus only on high-level video understanding, making it difficult to accurately assess and interpret the ability of VLMs to understand complex and fine-grained human activities. Inspired by the recently proposed MOMA framework, we define activity graphs as a single universal representation of human activities that encompasses video understanding at the activity, sub10 activity, and atomic action level. We redefine activity parsing as the overarching task of activity graph generation, requiring understanding human activities across all three levels. To facilitate the evaluation of models on activity parsing, we introduce MOMA-LRG (Multi-Object Multi-Actor Language-Refined Graphs), a large dataset of complex human activities with activity graph annotations that can be readily transformed into natural language sentences. Lastly, we present a model-agnostic and lightweight approach to adapting and evaluating VLMs by incorporating structured knowledge from activity graphs into VLMs, addressing the individual limitations of language and graphical models. We demonstrate a strong performance on activity parsing and few-shot video classification, and our framework is intended to foster future research in the joint modeling of videos, graphs, and language.more » « less
-
Fisman, Dana; Rosu, Grigore (Ed.)Fortran is widely used in computational science, engineering, and high performance computing. This paper presents an extension to the CIVL verification framework to check correctness properties of Fortran programs. Unlike previous work that translates Fortran to C, LLVM IR, or other intermediate formats before verification, our work allows CIVL to directly consume Fortran source files. We extended the parsing, translation, and analysis phases to support Fortran-specific features such as array slicing and reshaping, and to find program violations that are specific to Fortran, such as argument aliasing rule violations, invalid use of variable and function attributes, or defects due to Fortran's unspecified expression evaluation order. We demonstrate the usefulness of our tool on a verification benchmark suite and kernels extracted from a real world application.more » « less
-
Effective argumentation is essential towards a purposeful conversation with a satisfactory outcome. For example, persuading someone to reconsider smoking might involve empathetic, well founded arguments based on facts and expert opinions about its ill-effects and the consequences on one’s family. However, the automatic generation of high-quality factual arguments can be challenging. Addressing existing controllability issues can make the recent advances in computational models for argument generation a potential solution. In this paper, we introduce ArgU: a neural argument generator capable of producing factual arguments from input facts and real-world concepts that can be explicitly controlled for stance and argument structure using Walton’s argument scheme-based control codes. Unfortunately, computational argument generation is a relatively new field and lacks datasets conducive to training. Hence, we have compiled and released an annotated corpora of 69,428 arguments spanning six topics and six argument schemes, making it the largest publicly available corpus for identifying argument schemes; the paper details our annotation and dataset creation framework. We further experiment with an argument generation strategy that establishes an inference strategy by generating an “argument template” before actual argument generation. Our results demonstrate that it is possible to automatically generate diverse arguments exhibiting different inference patterns for the same set of facts by using control codes based on argument schemes and stance.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
