Disagreements are frequently studied from the perspective of either detecting
toxicity or analysing argument structure. We propose a framework of dispute
tactics that unifies these two perspectives, as well as other dialogue acts
which play a role in resolving disputes, such as asking questions and providing
clarification. This framework includes a preferential ordering among
rebuttal-type tactics, ranging from ad hominem attacks to refuting the central
argument. Using this framework, we annotate 213 disagreements (3,865
utterances) from Wikipedia Talk pages. This allows us to investigate research
questions around the tactics used in disagreements; for instance, we provide
empirical validation of the approach to disagreement recommended by Wikipedia.
We develop models for multilabel prediction of dispute tactics in an utterance,
achieving the best performance with a transformer-based label powerset model.
Adding an auxiliary task to incorporate the ordering of rebuttal tactics
further yields a statistically significant increase. Finally, we show that
these annotations can be used to provide useful additional signals to improve
performance on the task of predicting escalation.

我们提出了一个辩论战术的框架，用于统一争议的各种方面，并注释了来自维基百科讨论页面的 213 个争议，使用变压器模型是预测争议战术的最佳选择。

如何有效地进行不同意：研究维基百科争论中使用的策略

How to disagree well: Investigating the dispute tactics used on Wikipedia

We study non-collaborative dialogs, where two agents have a conflict of
interest but must strategically communicate to reach an agreement (e.g.,
negotiation). This setting poses new challenges for modeling dialog history
because the dialog's outcome relies not only on the semantic intent, but also
on tactics that convey the intent. We propose to model both semantic and tactic
history using finite state transducers (FSTs). Unlike RNN, FSTs can explicitly
represent dialog history through all the states traversed, facilitating
interpretability of dialog structure. We train FSTs on a set of strategies and
tactics used in negotiation dialogs. The trained FSTs show plausible tactic
structure and can be generalized to other non-collaborative domains (e.g.,
persuasion). We evaluate the FSTs by incorporating them in an automated
negotiating system that attempts to sell products and a persuasion system that
persuades people to donate to a charity. Experiments show that explicitly
modeling both semantic and tactic history is an effective way to improve both
dialog policy planning and generation performance.

通过有限状态转换器建模语义和策略历史记录，以改善非合作对话的效率和结构。

用明确的语义和战略对话历史增强非协作对话系统

Augmenting Non-Collaborative Dialog Systems with Explicit Semantic and  Strategic Dialog History

Humans prove theorems by relying on substantial high-level reasoning and
problem-specific insights. Proof assistants offer a formalism that resembles
human mathematical reasoning, representing theorems in higher-order logic and
proofs as high-level tactics. However, human experts have to construct proofs
manually by entering tactics into the proof assistant. In this paper, we study
the problem of using machine learning to automate the interaction with proof
assistants. We construct CoqGym, a large-scale dataset and learning environment
containing 71K human-written proofs from 123 projects developed with the Coq
proof assistant. We develop ASTactic, a deep learning-based model that
generates tactics as programs in the form of abstract syntax trees (ASTs).
Experiments show that ASTactic trained on CoqGym can generate effective tactics
and can be used to prove new theorems not previously provable by automated
methods. Code is available at this https URL

本文提出了使用机器学习自动化证明助手交互的问题。研究人员通过构建 CoqGym 数据集和 ASTactic 模型，能够生成高效的策略程序并用于自动证明定理。

通过与证明助手交互学习证明定理

Learning to Prove Theorems via Interacting with Proof Assistants

Technology offers new ways to measure the locations of the players and of the
ball in sports. This translates to the trajectories the ball takes on the field
as a result of the tactics the team applies. The challenge professionals in
soccer are facing is to take the reverse path: given the trajectories of the
ball is it possible to infer the underlying strategy/tactic of a team? We
propose a method based on Dynamic Time Warping to reveal the tactics of a team
through the analysis of repeating series of events. Based on the analysis of an
entire season, we derive insights such as passing strategies for maintaining
ball possession or counter attacks, and passing styles with a focus on the team
or on the capabilities of the individual players.

本文介绍了一种基于 Dynamic Time Warping 的方法，通过分析事件的重复序列来揭示球队的战术，并基于整个赛季的分析得出了保持球权的传球策略或反击，以及以团队为重点或以个人球员的能力为重点的传球风格等见解。