Source-linked AI summary
Conclusion-based Counter-Argument Generation
Milad Alshomary, Henning Wachsmuth
TL;DR
Automatic counter-argument generation often does not model an argument’s conclusion, which may be implicit despite being a common target for rebuttal. This paper jointly generates a conclusion and counter, using stance-based ranking; it reports better counters and more valid conclusions than baselines.
Problem
Existing counter-argument generation does not address the conclusion relation, although conclusions are often implicit and direct rebuttal is common.
Source: §1 · p. 1Method
The approach jointly learns to generate an argument’s conclusion and counter, then ranks candidate counters by stance toward the generated conclusion.
Source: §1 · p. 2, §3.1 · p. 3Results
Takeaways & Limitations
The results support modeling the argument’s conclusion as part of counter-argument generation, including when that conclusion is implicit.
Source: §5 · pp. 8–9Experiments use only BART, and the approach assumes ChangeMyView commenters rebut the original post’s conclusion, which may not always hold.
Source: §6 · p. 9
Abstract
from arXiv · showhide
In real-world debates, the most common way to counter an argument is to reason against its main point, that is, its conclusion. Existing work on the automatic generation of natural language counter-arguments does not address the relation to the conclusion, possibly because many arguments leave their conclusion implicit. In this paper, we hypothesize that the key to effective counter-argument generation is to explicitly model the argument's conclusion and to ensure that the stance of the generated counter is opposite to that conclusion. In particular, we propose a multitask approach that jointly learns to generate both the conclusion and the counter of an input argument. The approach employs a stance-based ranking component that selects the counter from a diverse set of generated candidates whose stance best opposes the generated conclusion. In both automatic and manual evaluation, we provide evidence that our approach generates more relevant and stance-adhering counters than strong baselines.
1 Introduction
Counter-argument generation is difficult when an argument’s conclusion is implicit, even though directly rebutting that conclusion is a common way to counter an argument. The paper jointly generates conclusions and counters, then ranks candidate counters by how well their stance opposes the generated conclusion.
1 Introduction
- Existing generation methods often omit the relation between premises and conclusions, making counter generation challenging when conclusions are implicit.The paper notes that machines find it challenging to infer a conclusion that people may recover from context.Source: §1 · p. 1
- The approach jointly models conclusion and counter generation and uses stance-based ranking to select counters opposing the generated conclusion.It explores two variants: one shares encoder and decoder and emits both outputs in one sequence; the other shares an encoder and uses separate decoders.Source: §1 · p. 2
- In most cases, the shared-encoder-and-decoder variation produces the best counters for relevance and stance correctness.The introduction also reports that standard transformer models are deficient, particularly when the conclusion is not mentioned explicitly.Source: §1 · p. 2
- The study targets effective counter generation even when the attacked argument’s conclusion is implicit.Its stated contributions include two transformer-based multitask approaches tuned to oppose the argument’s conclusion and analysis of joint modeling’s impact.Source: §1 · p. 2
2 Related Work
Prior counter-argument synthesis uses retrieval or generation, while conclusion inference is a related task because arguments often leave their main point implicit. This work uses generated conclusions as an initial step toward generating counters.
2 Related Work
- Retrieval approaches seek relevant counters from collections, while generation approaches construct counters from scratch.Retrieval depends on available counter-arguments and is limited by collection size; generation approaches include rule-based, neural, and retrieval-guided methods.Source: §2 · pp. 2–3
- Prior generation approaches may require an explicit conclusion or fail to distinguish argumentative components in the input.Some approaches generate counter-arguments by identifying weak points given a conclusion, while others use retrieved key phrases to guide long-text generation.Source: §2 · pp. 2–3
- Conclusion inference reconstructs the main point from premises, a task that remains challenging for machines when conclusions are implicit.Earlier work studied implicit claim-target reconstruction, transformer-based conclusion generation, and conclusion generation for argument-quality research.Source: §2 · p. 3
3 Approach
The approach jointly generates an argument’s conclusion and counter, then uses stance-based ranking to select a contrastive candidate. It explores shared and separate decoder designs for generating the two outputs.
3 Approach
- The approach jointly learns conclusion and counter generation, then ranks candidates to select one whose stance contrasts with the generated conclusion.Figure 1 summarizes the shared-generation alternatives and inference-time classification and ranking.Source: §3 · p. 3, Figure 1 · p. 4
3.1 Joint Generation of Conclusions and Counters
- For one-sequence generation, the model can use premises to generate diverse conclusions and corresponding counters by prompting with Wikipedia concepts and applying nucleus sampling.The approach also generates a conclusion and candidate counters through nucleus sampling without concept prompting.Source: §3.1 · pp. 3–4
- The two-sequence model shares an encoder but uses separate decoders to generate the conclusion and counter-argument.The fully shared alternative generates both outputs in one sequence, with the conclusion preceding the counter.Source: §3.1 · pp. 3–4
3.2 Ranking Component
- At inference, candidate counters are ranked by stance contrastiveness toward their corresponding generated conclusions, and the top-ranked counter is selected.The stance classifier scores each counter sentence; the counter’s final score is averaged across sentences and ranges from -1 to 1.Source: §3.2 · pp. 4–5
4 Experiments
The experiments test whether joint conclusion-and-counter generation with stance ranking improves counters when conclusions are implicit. On ChangeMyView, automatic and human evaluations favor the one-sequence stance-ranked model for counter quality, while the two-decoder model yields the strongest generated-conclusion validity.
4.1 Data
- On CMV, titles are conclusions, bodies premises, and comments counters; the highest predicted-quality comment is selected per post.For implicit-conclusion tests, only the body is input and the title is generated.Source: §4.1 · p. 5
4.2 Models
- The comparison includes joint models, a pipeline that learns conclusions and counters independently, a stance-ranked pipeline, and an oracle given ground-truth conclusions.Table 2 compares the approaches with and without stance ranking against baselines on counter similarity and opposing stance.Source: §4.2 · p. 6, Table 2 · p. 6
4.3 Automatic Evaluation
- Joint One-seq w/ Stance achieves stance score 0.889 and contrastiveness score 0.661, the best results when conclusions must be inferred; joint models outperform baselines in correct stance.Adding stance ranking consistently boosts correctness for the joint models and the pipeline baseline.Source: §4.3 · pp. 6–7, Table 2 · p. 6
- 0.53 to 0.43 is the baseline’s contrastiveness drop as arguments lengthen, while Joint One-seq scores 0.64–0.73; BERT F1 falls across all approaches with length.As conclusions become more explicit in the premises, BART-based w/o Conclusion improves in contrastiveness, while BART-based w/ Conclusion moves in the opposite direction; the proposed approach maintains high scores.Source: Figure 2 · p. 6, §4.3 · p. 7
4.4 Manual Evaluation
- With majority agreement, Joint One-seq w/ Stance ranks best (mean rank 2.26), with first- or second-place counters in 55% of cases.Joint Two-decoders w/ Stance averages 2.72; the stance-ranked pipeline is almost on par with the joint approaches.Source: §4.4 · p. 8, Table 3 · p. 8, Figure 3 · p. 8
- 2.03 was the validity score for Joint Two-decoders w/ Stance, versus 1.42 for the pipeline and 0.36 points below ground truth.The authors suggest that training may favor the easier conclusion-generation task and that task weighting could alleviate the counter-generation weakness.Source: §4.4 · p. 8, Table 4 · p. 8
5 Conclusion
The paper studies how an argument’s conclusion shapes counter-argument generation and proposes jointly generating conclusions and counters. Its approach uses stance-based ranking, and the authors report better counters and more valid conclusions than baselines.
5 Conclusion
- Jointly generating an argument’s conclusion and counter is presented as a way to address the challenge of implicit conclusions.The paper compares this approach with baselines that do not explicitly model conclusions.Source: §5 · p. 8
- The approach uses transformer-based models and a stance-based ranking component to ensure generated counters have a correct stance.The paper realizes the approach in two ways.Source: §5 · p. 9
- The authors report that joint learning produces better counters and more valid conclusions than the baselines, though results remain far from perfect.Source: §5 · p. 9
6 Limitation
The experiments use only BART as the underlying transformer, do not explore all multitask weighting schemes, and rely on a dataset assumption that may not always hold.
6 Limitation
- Experiments use only BART, so they do not establish whether conclusion modeling gains extend across transformer-based models.The authors suggest GPT and T5 as models for a stronger experiment setting.Source: §6 · p. 9
- The study does not explore all possible weighting schemes for the jointly learned tasks.The authors identify more systematic evaluation of weighting schemes as a potential direction.Source: §6 · p. 9
- The models are limited by data quality and assume that ChangeMyView commentators rebut the original post’s conclusion, which may not always hold.Source: §6 · p. 9
7 Ethics
The authors describe counter-argument generation as unresolved and flag faithfulness as a concern when language models generate text. They frame their experiments narrowly as highlighting the potential of conclusion inference in the generation pipeline.
7 Ethics
- The authors consider counter-argument generation far from resolved.Source: §7 · p. 9
- They state that faithful text generation must be considered when using language models to avoid misinformation.They propose that a fact-checking component should accommodate any text generation model.Source: §7 · p. 9
- The experiments’ stated goal is to highlight the potential of conclusion inference within the counter-argument generation pipeline.Source: §7 · p. 9
A Example Analysis
The example analysis contrasts generated counters: the joint model produces a counter addressing the inferred conclusion, while baselines show problems with relevance, clarity, or opposition. Annotator comments associate the joint model’s stronger outputs with coherent argumentation and sometimes new perspectives.
A Example Analysis
- The BART baseline without conclusion modeling rephrases input sentences, while the pipeline baseline’s counter remains vague and does not clearly oppose the argument’s stance.Source: A Example Analysis · pp. 10–11
- In the example, Joint-based One-seq infers the main point as “Scientific law” and counters it by noting that intelligence is difficult to define and measure.Source: A Example Analysis · p. 11
- Annotators most appreciated Joint-based One-seq when it produced argumentative, coherent counters that sometimes offered new perspectives.Worst-ranked outputs were mainly vague, incoherent, or off-topic; the no-conclusion baseline was also often criticized for not opposing the input argument.Source: A Example Analysis · p. 11
B Computing Infrastructure
Experiments ran on Ubuntu 20.04 with Python 3.8.10 and CUDA 11.2, using one A100-SXM4-40GB GPU.
B Computing Infrastructure
- Experiments ran on Ubuntu 20.04 with Python 3.8.10 and CUDA 11.2, using one A100-SXM4-40GB GPU.The section lists the computing environment used to train the models.Source: B Computing Infrastructure · p. 11