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Neural Argument Generation Augmented with Externally Retrieved Evidence
Computation and Language · Submitted 25 May 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than a popular sequence-to-sequence generation model according to both automatic evaluation and human assessments.
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The abstract, sentence by sentence
High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose an encoder-decoder style neural network-based argument generation model enriched with externally retrieved evidence from Wikipedia. Our model first generates a set of talking point phrases as intermediate representation, followed by a separate decoder producing the final argument based on both input and the keyphrases. Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than a popular sequence-to-sequence generation model according to both automatic evaluation and human assessments.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | High quality arguments are essential elements for human reasoning and decision-making processes. | background 1.00 | 0.03 | 0.02 | asserted 1.00 |
| s1 | However, effective argument construction is a challenging task for both human and machines. | background 0.91 | 0.05 | 0.02 | asserted 1.00 |
| s2 | In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. | contribution 0.98 | 0.55 | 0.04 | asserted 0.91 |
| s3 | We propose an encoder-decoder style neural network-based argument generation model enriched with externally retrieved evidence from Wikipedia. | method 0.83 | 0.86 | 0.06 | asserted 0.99 |
| s4 | Our model first generates a set of talking point phrases as intermediate representation, followed by a separate decoder producing the final argument based on both input and the keyphrases. | method 1.00 | 0.87 | 0.05 | asserted 0.99 |
| s5 | Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than a popular sequence-to-sequence generation model according to both automatic evaluation and human assessments. | result 1.00 | 0.96 | 0.83 | asserted 1.00 |
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The paper
Original abstract
High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose an encoder-decoder style neural network-based argument generation model enriched with externally retrieved evidence from Wikipedia. Our model first generates a set of talking point phrases as intermediate representation, followed by a separate decoder producing the final argument based on both input and the keyphrases. Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than a popular sequence-to-sequence generation model according to both automatic evaluation and human assessments.
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How the literature uses it
3 arXiv papers cite this in 3 sentences
Papers citing it · 1 sentence without a confident label
Cites it as background 2 papers
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Recent work has made progress towards this goal through the automated generation of argumentative text [ref] .
HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine · Argumentation mining and generation · 0.80
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In the field of NLP, previous works have addressed fallacy identification [ref] , CA retrieval [ref] , and CA generation for macro-level arguments [ref] , and essay criteria such as thesis clarity [ref] , argument strength [ref] , and stance [ref] have been evaluated.
Riposte! A Large Corpus of Counter-Arguments · Introduction · 0.99
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