Tool learning, which allows Large Language Models (LLMs) to leverage external tools for solving complex user tasks, has emerged as a promising avenue for extending model capabilities. However, existing approaches primarily focus on data synthesis for fine-tuning LLMs to invoke tools effectively, largely ignoring how to fully stimulate the potential of the model. In this paper, we propose ToolACE-R, a novel framework that includes both model-aware iterative training and adaptive refinement for tool learning. ToolACE-R features a model-aware iterative training procedure that progressively adjust training samples based on the model’s evolving capabilities to maximize its potential. Additionally, it incorporates self-refinement training corpus which emphasizes LLM's ability to iteratively refine their tool calls, optimizing performance without requiring external feedback. Furthermore, we introduce adaptive self-refinement for efficient test-time scaling, where the trained model can autonomously determine when to stop the process based on iterative self-refinement. We conduct extensive experiments across several benchmark datasets, showing that ToolACE-R achieves competitive performance compared to advanced LLMs. The performance can be further improved efficiently through adaptive self-refinement. These results highlight the effectiveness and generalizability of ToolACE-R, offering a promising direction for more efficient and scalable tool learning.

Emotional Support Conversation (ESC) plays a critical role in mental health assistance by providing accessible psychological support in real-world applications. Large Language Models (LLMs) have shown strong empathetic abilities in ESC tasks. Yet, existing methods overlook the issue of cognitive distortions in help-seekers’ expressions. As a result, current models can only provide basic emotional comfort, rather than helping help-seekers address their psychological distress at a deeper cognitive level. To address this challenge, we construct the CogBiasESC dataset, the first dataset that expands existing ESC datasets by adding labels for cognitive distortions, includes their type, intensity, and safe risk level. Furthermore, we propose the Cognitive Policy-driven Large Language Model framework (CoPoLLM) to enhance LLMs’ ability to diagnose and intervene cognitive distortions in help-seekers. We also analyze the safety advantages of CoPoLLM from a theoretical perspective. Experimental results show that CoPoLLM significantly outperforms 15 state-of-the-art baselines in terms of distortion diagnosis accuracy, intervention strategy effectiveness, and safety risk control. Our source code is available at: https://github.com/Chips98/CoPoLLM-for-ACL-2026.

Recently, Heterogeneous Graph Contrastive Learning (HGCL) has received significant attention due to its impressive capability to represent heterogeneous graphs without detailed annotations. However, the inherent fragility of heterogeneous graph structures makes HGCL vulnerable to perturbation attacks. Most existing defense works for heterogeneous graphs primarily focus on supervised scenarios, which protect all nodes equally via structural pruning. This defensive mechanism can result in insufficient structure information for HGCL, thus degrading performance in self-supervised scenarios without labels. In this paper, we argue that some nodes are more susceptible to attacks, and the influence of the perturbation attack will accumulate across layers during representation aggregation. To tackle these problems, we propose a novel Heterogeneous Graph Contrastive Learning with Hierarchical Vulnerability Protection (HVP-HGCL), which identifies the most vulnerable nodes to perturbation attack and protects them across different aggregation layers to improve the robustness of HGCL. Specifically, we first design the Vulnerability Detection (VD) based on the HGCL framework to determine which nodes are more sensitive to attack in self-supervised scenarios. Subsequently, we propose a simple but efficient Hierarchical Protection (HP) to safeguard those vulnerable nodes from attack noise during different layers. Combining the above two modules, HVP-HGCL can not only improve the robustness of HGCL but also ensure sufficient structural information for effective contrastive learning. Extensive experiments demonstrate that HVP-HGCL improves robustness against adversarial attacks and achieves competitive performance on downstream tasks.

Subject-driven text-to-image generation aims to generate customized high-fidelity images based on text descriptions for specific subjects, which has gained increasing attention. Despite recent advancements in single-subject customization, existing methods often struggle with multi-subject scenarios, leading to distortions in subject identity. This challenge arises because entangled identity-relevant and irrelevant information can obscure subject identities, and inter-subject interference can cause confusion or loss of individual identities. To address these issues, we propose CausalT2I, a customized multi-subject text-to-image generation framework with causal tuning. First, we propose asubject-aware causal disentanglementmethod, which can self-adaptively distinguish causally relevant and irrelevant information for subjects through causal intervention and a causal disentangled objective. Then, we design asoft cross-attention guidancestrategy to mitigate interference among different subjects by aligning the textual attributes of each subject with its identity-relevant visual attributes. Last, we introduce acausal denoising objectiveto optimize the denoising process using identity-preserved textual embeddings and identity-irrelevant visual embeddings. Extensive experiments show that CausalT2I has superior generation ability in subject-driven text-to-image generation over existing baseline methods and brings more flexibility and controllability for generating customized multi-subject images.

In federated recommendation systems, model poisoning attacks aim to manipulate the gradient information of multiple target items sent back from local clients to the central server, with the goal of abnormally increasing their exposure across the system. Existing multi-target attack approaches directly manipulate multiple target items and apply a uniform attack strategy to all target items, which may lead to suboptimal promotion effectiveness. To address this issue, we introduce ProitMTA, a novel multi-target model poisoning attack framework that introduces proxy items and provides tailored attack strategies for target items. ProitMTA employs a three-stage process that balances the promotion of multiple target items while preserving recommendation quality. First,proxy item generationuses a Gaussian Mixture Model to create proxy items that represent diverse attack strategies. Second,proxy attack constructiondesigns customized gradient manipulation strategies for each proxy item. Finally,proxy-based target item attacktransfers these strategies to actual target items, enhancing their promotion while minimizing the negative impact on system performance. Through comprehensive experiments on multiple base federated recommendation frameworks and diverse real-world datasets, we demonstrate that ProitMTA outperforms existing attack methods, achieving higher success rates in target item promotion with minimal system-wide performance degradation. Our research highlights the vulnerability of federated recommendation systems when facing multi-target poisoning attacks and underscores the importance of researching effective defense mechanisms We have released our code athttps://github.com/zdy769243418/ProitMTA.

This paper explores Machine Unlearning (MU), an emerging field that is gaining increased attention due to concerns about neural models unintentionally remembering personal or sensitive information. We present SeUL, a novel method that enables selective and fine-grained unlearning for language models. Unlike previous work that employs a fully reversed training objective in unlearning, SeUL minimizes the negative impact on the capability of language models, particularly in terms of generation. Furthermore, we introduce two innovative evaluation metrics, sensitive extraction likelihood (S-EL) and sensitive memorization accuracy (S-MA), specifically designed to assess the effectiveness of forgetting sensitive information. In support of the unlearning framework, we propose efficient automatic online and offline sensitive span annotation methods. The online selection method, based on language probability scores, ensures computational efficiency, while the offline annotation involves a two-stage LLM-based process for robust verification. In summary, this paper contributes a novel selective unlearning method (SeUL), introduces specialized evaluation metrics (S-EL and S-MA) for assessing sensitive information forgetting, and proposes automatic online and offline sensitive span annotation methods to support the overall unlearning framework and evaluation.

The pervasive spread of misinformation and disinformation in social media underscores the critical importance of detecting media bias. While robust Large Language Models (LLMs) have emerged as foundational tools for bias prediction, concerns about inherent biases within these models persist. In this work, we investigate the presence and nature of bias within LLMs and its consequential impact on media bias detection. Departing from conventional approaches that focus solely on bias detection in media content, we delve into biases within the LLM systems themselves. Through meticulous examination, we probe whether LLMs exhibit biases, particularly in political bias prediction and text continuation tasks. Additionally, we explore bias across diverse topics, aiming to uncover nuanced variations in bias expression within the LLM framework. Importantly, we propose debiasing strategies, including prompt engineering and model fine-tuning. Extensive analysis of bias tendencies across different LLMs sheds light on the broader landscape of bias propagation in language models. This study advances our understanding of LLM bias, offering critical insights into its implications for bias detection tasks and paving the way for more robust and equitable AI systems

Cross-domain sequential recommendation (CSR) has garnered significant attention. Current federated frameworks for CSR leverage information across multiple domains but often rely on user alignment, which increases communication costs and privacy risks. In this work, we propose FedCSR, a novel federated cross-domain sequential recommendation framework that eliminates the need for user alignment between platforms. FedCSR fully utilizes cross-domain knowledge to address the key challenges related to data heterogeneity both inter- and intra-platform. To tackle the heterogeneity of data patterns between platforms, we introduce Model Contrastive Learning (MCL) to reduce the gap between local and global models. Additionally, we design Sequence Contrastive Learning (SCL) to address the heterogeneity of user preferences across different domains within a platform by employing tailored sequence augmentation techniques. Extensive experiments conducted on multiple real-world datasets demonstrate that FedCSR achieves superior performance compared to existing baseline methods.

Multi-task Learning (MTL) involves training multiple tasks within a single model to improve overall performance by leveraging shared knowledge. However, this joint training can result in performance degradation due to task conflicts, typically manifesting as conflicts in task gradients. Existing solutions primarily focus on modeling task gradient relationships, which overlook the differences in how the same data sample influences different tasks. These differences are the source of intricate task gradient relationships and could further lead to varying degrees of impact from conflicts on tasks. To tackle these challenges, we propose DTJS, a novel adaptive Data and Task Joint Scheduling approach for MTL, which uniquely considers the influence of data within each task and the distinct task perception of gradient conflicts from an innovative scheduling perspective. Specifically, we design intra-task scheduling to quantify the difficulty level of the data based on its influence within each task, facilitating easy-to-hard data scheduling. Concurrently, inter-task scheduling is proposed to capture the diverse relationship among joint learning tasks via assessing the severity of conflicts between tasks and adaptively considering their effects on individual tasks through learnable task conflict perception. Furthermore, DTJS utilizes a bi-level optimization strategy that alternately updates model parameters and the learnable task conflict perception, taking into account their interdependence. Scheduled model gradients are used to optimize the MTL model, while implicit gradients refine the learnable task conflict perception. Extensive experimental results not only demonstrate that DTJS improves the performance of the MTL model over SOTA methods across various scenarios but also explain how DTJS schedules both data and tasks to bring performance improvements. The code is available at https://github.com/ZeyuLiu0706IDTJS.

In the age of information overload and polarized discourse, understanding media bias has become imperative for informed decision-making and fostering a balanced public discourse. However, without the experts' analysis, it is hard for the readers to distinguish bias from the news articles. This paper presents IndiTag, an innovative online media bias analysis system that leverages fine-grained bias indicators to dissect and distinguish bias in digital content. IndiTag offers a novel approach by incorporating large language models, bias indicators, and vector database to detect and interpret bias automatically. Complemented by a user-friendly interface facilitating automated bias analysis for readers, IndiTag offers a comprehensive platform for in-depth bias examination. We demonstrate the efficacy and versatility of IndiTag through experiments on four datasets encompassing news articles from diverse platforms. Furthermore, we discuss potential applications of IndiTag in fostering media literacy, facilitating fact-checking initiatives, and enhancing the transparency and accountability of digital media platforms. IndiTag stands as a valuable tool in the pursuit of fostering a more informed, discerning, and inclusive public discourse in the digital age. We release an online system for end users and the source code is available at https://github.com/lylin0/IndiTag.

Conversational Recommender Systems (CRS) have become increasingly important due to their ability to recommend items through interactive dialogue, adapting to user preferences in real time. Traditional CRS approaches face challenges in generating high-quality, diverse responses due to the limited availability of training data and the inherited biases from domain-specific fine-tuning. Furthermore, existing systems often overlook the impact of confounding variables during user interactions, leading to suboptimal recommendations. In this work, we propose a novel hybrid framework that integrates large language models (LLMs) with traditional recommendation techniques to address these limitations. Our approach leverages the strengths of LLMs in generating fluent, contextually appropriate responses while employing a traditional recommendation module to capture complex interaction structures. To ensure unbiased recommendations, we introduce causal interventions that disentangle confounding variables, improving recommendation accuracy. We evaluate our framework on established CRS datasets, demonstrating significant improvements in recommendation quality and response generation. Our results highlight the effectiveness of the causal intervention mechanism in producing more reliable and personalized recommendations, while the LLM-based response generation offers scalability across multiple domains.

Domain heterogeneity in federated learning presents significant challenges, particularly in complex tasks like semantic segmentation, where pixel-level accuracy is crucial. Existing approaches primarily focus on extracting domain-specific features, but they often overlook the importance of class-level feature invariance. In this paper, we propose FedSS, a novel federated semantic segmentation framework designed to address domain heterogeneity. FedSS introduces a Fine-grained Domain-agnostic Feature Extraction (DFE) module that standardizes feature maps using category-level statistics to ensure domain-agnostic feature extraction. Additionally, the Adaptive Ordering Based Feature Decorrelation (AFD) module enhances the model’s ability to distinguish between different semantic categories by decorrelating feature channels. To further tackle domain discrepancies, we present a Domain Fairness-aware Aggregation Strategy (DFA) that dynamically adjusts aggregation weights based on local domain variations. Our approach improves the robustness and generalization of the semantic segmentation model across diverse domains, ensuring more accurate and reliable pixel-level predictions. Experimental results demonstrate the effectiveness of FedSS in addressing domain heterogeneity and enhancing segmentation performance in federated learning settings. The source code is released at https://github.com/vibratingwings/FedSemanSeg

Current parameter-efficient fine-tuning (PEFT) methods have shown superior performance in continual learning. However, most existing PEFT-based methods focus on mitigating catastrophic forgetting by limiting modifications to the old task model caused by new tasks. This hinders backward knowledge transfer, as when new tasks have a strong positive correlation with old tasks, appropriately training on new tasks can transfer beneficial knowledge to old tasks. Critically, achieving backward knowledge transfer faces two fundamental challenges: (1) some parameters may be ineffective on task performance, which constrains the task solution space and model capacity; (2) since old task data are inaccessible, modeling task correlation via shared data is infeasible. To address these challenges, we propose CaLoRA, a novel \textbf{c}ausal-\textbf{a}ware \textbf{lo}w-\textbf{r}ank \textbf{a}daptation framework that is the first PEFT-based continual learning work with backward knowledge transfer. Specifically, we first propose \textbf{p}ar\textbf{a}meter-level \textbf{c}ounterfactual \textbf{a}ttribution (PaCA) that estimates the causal effect of LoRA parameters via counterfactual reasoning, identifying effective parameters from a causal view. Second, we propose \textbf{c}ross-t\textbf{a}sk \textbf{g}radient \textbf{a}daptation (CaGA) to quantify task correlation by gradient projection and evaluate task affinity based on gradient similarity. By incorporating causal effect, task correlation, and affinity, CaGA adaptively adjusts task gradients, facilitating backward knowledge transfer without relying on data replay. Extensive experiments across multiple benchmarks and continual learning settings show that CaLoRA outperforms state-of-the-art methods. In particular, CaLoRA better mitigates catastrophic forgetting by enabling positive backward knowledge transfer.

Recent studies reveal that Large Language Models (LLMs) exhibit strong sequential reasoning capabilities, allowing them to replace specialized time-series models and serve as foundation models for complex time-series analysis. To activate the capabilities of LLMs for time-series tasks, numerous studies have attempted to bridge the gap between time series and linguistics by aligning textual representations with time-series patterns. However, it is a non-trivial endeavor to losslessly capture the infinite time-domain variability using natural language, leading to suboptimal alignment performance. Beyond representation, contextual differences, where semantics in time series are conveyed by consecutive points, unlike in text by individual tokens, are often overlooked by existing methods. To address these, we propose S$^2$TS-LLM, a simple yet effective framework to repurpose LLMs for universal time series analysis through the following two main paradigms: (i) a spectral symbolization paradigm transforms time series into frequency-domain representations characterized by a fixed number of components and prominent amplitudes, which enables a limited set of symbols to effectively abstract key frequency features; (ii) a contextual segmentation paradigm partitions the sequence into blocks based on temporal patterns and reassigns positional encodings accordingly, thereby mitigating the structural mismatch between time series and natural language. Together, these paradigms bootstrap the LLMs' perception of temporal patterns and structures, effectively bridging time series and linguistics. Extensive experiments show that S$^2$TS-LLM can serve as a powerful time series analyzer, outperforming state-of-the-art methods across time series tasks.

Large Language Models (LLMs) offer new opportunities for the next Point-Of-Interest (POI) prediction task, leveraging their capabilities in semantic understanding of POI trajectories. However, previous LLM-based methods, which are superficially adapted to next POI prediction, largely overlook critical challenges associated with applying LLMs to this task. Specifically, LLMs encounter two critical challenges: (1) a lack of intrinsic understanding of numeric spatiotemporal data, which hinders accurate modeling of users' spatiotemporal distributions and preferences; and (2) an excessively large and unconstrained candidate POI space, which often results in random or irrelevant predictions. To address these issues, we propose a Collaborative Multi-Agent Framework for Next POI Prediction, named CoMaPOI. Through the close interaction of three specialized agents (Profiler, Forecaster, and Predictor), CoMaPOI collaboratively addresses the two critical challenges. The Profiler agent is responsible for converting numeric data into language descriptions, enhancing semantic understanding. The Forecaster agent focuses on dynamically constraining and refining the candidate POI space. The Predictor agent integrates this information to generate high-precision predictions. Extensive experiments on three benchmark datasets (NYC, TKY, and CA) demonstrate that CoMaPOI achieves state-of-the-art performance, improving all metrics by 5% to 10% compared to SOTA baselines. This work pioneers the investigation of challenges associated with applying LLMs to complex spatiotemporal tasks by leveraging tailored collaborative agents. Our source code is available at: https://github.com/Chips98/CoMaPOI.

Personalized federated learning (PFL) has become a hot research topic that can learn a personalized learning model for each client. Existing PFL models prefer to aggregate similar clients with similar data distribution to improve the performance of learning models. However, similarity-based PFL methods may exacerbate the class imbalance problem. In this article, we propose a novel dynamic affinity-based PFL (DA-PFL) model to alleviate the class imbalanced problem during federated learning. Specifically, we build an affinity metric from a complementary perspective to guide which clients should be aggregated. We then design a dynamic aggregation strategy that adjusts client aggregation based on the affinity metric in each round, thereby reducing the risk of class imbalance. Extensive experiments demonstrate that the proposed DA-PFL model can significantly improve the accuracy of each client in four real-world datasets with state-of-the-art comparison methods.

In an era characterized by the rapid proliferation of information, the pervasive issues of misinformation and disinformation have significantly impacted numerous individuals. Consequently, the evaluation of information’s truthfulness and accuracy has garnered substantial attention among researchers. In this work, we present a novel fact-checking framework called PACAR, fact-checking based on planning and customized action reasoning using LLMs. It comprises four modules: a claim decomposer with self-reflection, an LLM-centric planner module, an executor for carrying out planned actions, and a verifier module that assesses veracity and generates explanations based on the overall reasoning process. Unlike previous work that employs single-path decision-making and single-step verdict prediction, PACAR focuses on the use of LLMs in dynamic planning and execution of actions. Furthermore, in contrast to previous work that relied primarily on general reasoning, we introduce tailored actions such as numerical reasoning and entity disambiguation to effectively address potential challenges in fact-checking. Our PACAR framework, incorporating LLM-centric planning along with customized action reasoning, significantly outperforms baseline methods across three datasets from different domains and with varying complexity levels. Additional experiments, including multidimensional and sliced observations, demonstrate the effectiveness of PACAR and offer valuable insights for the advancement of automated fact-checking.

Text clustering is a fundamental task in natural language processing with numerous applications.However, traditional clustering methods often struggle with domain-specific fine-tuning and the presence of outliers.To address these challenges, we introduce LLMEdgeRefine, an iterative clustering method enhanced by large language models (LLMs), focusing on edge points refinement.LLMEdgeRefine enhances currrent clustering methods by creating superpoints to mitigate outliers and iteratively refining clusters using LLMs for improved semantic coherence.Our method demonstrates superior performance across multiple datasets, outperforming state-of-the-art techniques, and offering robustness, adaptability, and cost-efficiency for diverse text clustering applications.

Conversational Recommender Systems (CRS) has become an emerging research topic seeking to perform recommendations through interactive conversations, which generally consist of generation and recommendation modules. Prior work on CRS tends to incorporate more external and domain-specific knowledge like item reviews to enhance performance. Despite the fact that the collection and annotation of theexternal domain-specificinformation needs much human effort and degenerates the generalizability, too much extra knowledge introduces more difficulty to balance among them. Therefore, we propose to fully discover and extract theinternalknowledge from the context. We capture both entity-level and contextual-level representations to jointly model user preferences for the recommendation, where a time-aware attention is designed to emphasize the recently appeared items in entity-level representations. We further use the pre-trained BART to initialize the generation module to alleviate the data scarcity and enhance the context modeling. In addition to conducting experiments on a popular dataset (ReDial), we also include a multi-domain dataset (OpenDialKG) to show the effectiveness of our model. Experiments on both datasets show that our model achieves better performance on most evaluation metrics with less external knowledge and generalizes well to other domains. Additional analyses on the recommendation and generation tasks demonstrate the effectiveness of our model in different scenarios.

Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about specific training instances as if they have never existed in the training set.Previous work mainly focuses on computer vision scenarios and largely ignores the essentials of unlearning in NLP field, where text data contains more explicit and sensitive personal information than images.In this paper, we propose a general unlearning framework called KGA to induce forgetfulness.Different from previous work that tries to recover gradients or forces models to perform close to one specific distribution, KGA maintains distribution differences (i.e., knowledge gap).This relaxes the distribution assumption.Furthermore, we first apply the unlearning method to various NLP tasks (i.e., classification, translation, response generation) and propose several unlearning evaluation metrics with pertinence.Experiments on large-scale datasets show that KGA yields comprehensive improvements over baselines, where extensive analyses further validate the effectiveness of KGA and provide insight into unlearning for NLP tasks 1 .

Jakub Macina, Nico Daheim, Lingzhi Wang, Tanmay Sinha, Manu Kapur, Iryna Gurevych, Mrinmaya Sachan. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.

Quotations are crucial for successful explanations and persuasions in interpersonal communications. However, finding what to quote in a conversation is challenging for humans. This work studies automatic quotation recommendation for online conversations. Unlike the previous works that only consider semantic-level modeling, we adopt topic-level representation to facilitate the recommendation. A hierarchical architecture that is based on a pretrained language model is adopted to model the semantic-level conversation representation, and a neural topic model is employed to learn the topic-level representation. Moreover, the semantic-level conversation modeling is enhanced by a topic-aware attention mechanism, which is adopted to capture the interactive conversation structure from the perspective of word co-occurrence. The joint training of semantic- and topic-based recommendation leads to significantly better performance than the state-of-the-art models on two large-scale datasets. Apart from the novel and advanced recommendation framework, we conduct extensive quantitative experiments to investigate the difficulty of the quotation recommendation task, validate the topic-based recommendation assumption, and explore the stability of the recommendation. Some qualitative experiments and analyses are also included to interpret the quotation and topic distribution for some instances. All the extensive experiments and analyses provide persuasive explanations and interpretations of the module design and the recommendation results.

With the increasing popularity of social media, online interpersonal communication now plays an essential role in people’s everyday information exchange. Whether and how a newcomer can better engage in the community has attracted great interest due to its application in many scenarios. Although some prior works that explore early socialization have obtained salient achievements, they are focusing on sociological surveys based on the small group. To help individuals get through the early socialization period and engage well in online conversations, we study a novel task to foresee whether a newcomer’s message will be responded to by other participants in a multi-party conversation (henceforth Successful New-entry Prediction)1. The task would be an important part of the research in online assistants and social media. To further investigate the key factors indicating such engagement success, we employ an unsupervised neural network, Variational Auto-Encoder (VAE), to examine the topic content and discourse behavior from newcomer’s chatting history and conversation’s ongoing context. Furthermore, two large-scale datasets, from Reddit and Twitter, are collected to support further research on new-entries. Extensive experiments on both Twitter and Reddit datasets show that our model significantly outperforms all the baselines and popular neural models. Additional explainable and visual analyses on new-entry behavior shed light on how to better join in others’ discussions.

The popularity of social media platforms results in a huge volume of online conversations produced every day. To help users better engage in online conversations, this article presents a novel framework to automatically recommend conversations to users based on what they said and how they behaved in their chatting histories. While prior work mostly focuses on post-level recommendation, we aim to explore conversation context and model the interaction patterns therein. Furthermore, to characterize personal interests from interleaving user interactions, we learn (1) global interactions , represented by topic and discourse word clusters to reflect users’ content and pragmatic preferences, and (2) local interactions , encoding replying relations and chronological order of conversation turns to characterize users’ prior behavior. Built on collaborative filtering, our model captures global interactions via discovering word distributions to represent users’ topical interests and discourse behaviors, while local interactions are explored with graph-structured networks exploiting both reply structure and temporal features. Extensive experiments on three datasets from Twitter and Reddit show that our model coupling global and local interactions significantly outperforms the state-of-the-art model. Further analyses show that our model is able to capture meaningful features from global and local interactions, which results in its superior performance in conversation recommendation.

Quotations are crucial for successful explanations and persuasions in interpersonal communications.However, finding what to quote in a conversation is challenging for both humans and machines.This work studies automatic quotation generation in an online conversation and explores how language consistency affects whether a quotation fits the given context.Here, we capture the contextual consistency of a quotation in terms of latent topics, interactions with the dialogue history, and coherence to the query turn's existing content.Further, an encoder-decoder neural framework is employed to continue the context with a quotation via language generation.Experiment results on two large-scale datasets in English and Chinese demonstrate that our quotation generation model outperforms the state-of-the-art models.Further analysis shows that topic, interaction, and query consistency are all helpful to learn how to quote in online conversations.

Ming Liao, Jing Li, Haisong Zhang, Lingzhi Wang, Xixin Wu, Kam-Fai Wong. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.