Automated schedule generation for multitask from natural language descriptions has huge potential in modern industry.While classic methods bypass language complexities by using preformatted matrices, and recent LLM+solver approaches introduce new fragilities by relying on solver-specific code generation.This raises critical questions: Can large language models (LLMs) solve this NL ⇒ Schedule task end-to-end well (RQ1)?If the answer is "no", where do they fall short (RQ2)?And how can their capabilities be enhanced (RQ3)?To answer these questions, we introduce NL ⇒ Schedule, the first benchmark for this task, equipped with a dataset of 240 descriptionschedule pairs constructed from real-world materials and a rigorous evaluation suite.Our evaluation of nine state-of-the-art LLMs reveals the limitations of different LLMs in procedure grounding and the strengths of advanced LLMs in global planning via local analysis.To address these shortcomings, we propose MANS, a novel multi-agent framework.Extensive experiments show that MANS achieves more robust performance comparable to six state-ofthe-art LLM+solver methods.We hope NL ⇒ Schedule and MANS will serve as a solid foundation for automatic scheduling.The code and dataset are available in https://github.com/ SCUNLP/NL2Schedule

Xinwei Yang, Junyi Fan, Yuqing Liu, Jiaxuan Wang, Jiashuai Zhang, Hongru Liang, Wenqiang Lei, Yao Song. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Despite the great potential of large language models (LLMs) in machine comprehension, it is still disturbing to fully count on them in realworld scenarios.This is probably because there is no rational explanation for whether the comprehension process of LLMs is aligned with that of experts.In this paper, we propose SCOP to carefully examine how LLMs perform during the comprehension process from a cognitive view.Specifically, it is equipped with a systematical definition of five requisite skills during the comprehension process, a strict framework to construct testing data for these skills, and a detailed analysis of advanced open-sourced and closed-sourced LLMs using the testing data.With SCOP, we find that it is still challenging for LLMs to perform an expert-level comprehension process.Even so, we notice that LLMs share some similarities with experts, e.g., performing better at comprehending local information than global information.Further analysis reveals that LLMs can be somewhat unreliable -they might reach correct answers through flawed comprehension processes.Based on SCOP, we suggest that one direction for improving LLMs is to focus more on the comprehension process, ensuring all comprehension skills are thoroughly developed during training 1 .

Automatic extraction of procedural graphs from documents creates a low-cost way for users to easily understand a complex procedure by skimming visual graphs.Despite the progress in recent studies, it remains unanswered: whether the existing studies have well solved this task (Q1) and whether the emerging large language models (LLMs) can bring new opportunities to this task (Q2).To this end, we propose a new benchmark PAGED, equipped with a large high-quality dataset and standard evaluations.It investigates five state-of-the-art baselines, revealing that they fail to extract optimal procedural graphs well because of their heavy reliance on hand-written rules and limited available data.We further involve three advanced LLMs in PAGED and enhance them with a novel self-refine strategy.The results point out the advantages of LLMs in identifying textual elements and their gaps in building logical structures.We hope PAGED can serve as a major landmark for automatic procedural graph extraction and the investigations in PAGED can provide valuable insights into the research on logical reasoning among non-sequential elements.The code and dataset are available in https://github.com/SCUNLP/PAGED.

It is time-saving to build a reading assistant for customer service representations (CSRs) when reading user manuals, especially informationrich ones.Current solutions don't fit the online custom service scenarios well due to the lack of attention to user questions and possible responses.Hence, we propose to develop a timesaving and careful reading assistant for CSRs, named CARE.It can help the CSRs quickly find proper responses from the user manuals via explicit clue chains.Specifically, each of the clue chains is formed by inferring over the user manuals, starting from the question clue aligned with the user question and ending at a possible response.To overcome the shortage of supervised data, we adopt the self-supervised strategy for model learning.The offline experiment shows that CARE is efficient in automatically inferring accurate responses from the user manual.The online experiment further demonstrates the superiority of CARE to reduce CSRs' reading burden and keep high service quality, in particular with > 35% decrease in time spent and keeping a > 0.75 ICC score.

Tong Zhang, Peixin Qin, Yang Deng, Chen Huang, Wenqiang Lei, Junhong Liu, Dingnan Jin, Hongru Liang, Tat-Seng Chua. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

We investigate non-collaborative dialogue agents, which are expected to engage in strategic conversations with diverse users, for securing a mutual agreement that leans favorably towards the system's objectives.This poses two main challenges for existing dialogue agents:1) The inability to integrate user-specific characteristics into the strategic planning, and 2) The difficulty of training strategic planners that can be generalized to diverse users.To address these challenges, we propose TRIP to enhance the capability in tailored strategic planning, incorporating a user-aware strategic planning module and a population-based training paradigm.Through experiments on benchmark non-collaborative dialogue tasks, we demonstrate the effectiveness of TRIP in catering to diverse users.

Accurate knowledge selection is critical in knowledge-grounded dialogue systems.Towards a closer look at it, we offer a novel perspective to organize existing literature, i.e., knowledge selection coupled with, after, and before generation.We focus on the third underexplored category of study, which can not only select knowledge accurately in advance, but has the advantage to reduce the learning, adjustment, and interpretation burden of subsequent response generation models, especially LLMs.We propose GATE, a generator-agnostic knowledge selection method, to prepare knowledge for subsequent response generation models by selecting context-related knowledge among different knowledge structures and variable knowledge requirements.Experimental results demonstrate the superiority of GATE, and indicate that knowledge selection before generation is a lightweight yet effective way to facilitate LLMs (e.g., ChatGPT) to generate more informative responses.

Towards sufficient music searching, it is vital to form a complete set of labels for each song. However, current solutions fail to resolve it as they cannot produce diverse enough mappings to make up for the information missed by the gold labels. Based on the observation that such missing information may already be presented in user comments, we propose to study the automated music labeling in an essential but under-explored setting, where the model is required to harvest more diverse and valid labels from the users' comments given limited gold labels. To this end, we design an iterative framework (DiVa) to harvest more Diverse and Valid labels from user comments for music. The framework makes a classifier able to form complete sets of labels for songs via pseudo-labels inferred from pre-trained classifiers and a novel joint score function. The experiment on a densely annotated testing set reveals the superiority of the DiVa over state-of-the-art solutions in producing more diverse labels missed by the gold labels. We hope our work can inspire future research on automated music labeling.

Proactive dialogue system is able to lead the conversation to a goal topic and has advantaged potential in bargain, persuasion, and negotiation. Current corpus-based learning manner limits its practical application in real-world scenarios. To this end, we contribute to advancing the study of the proactive dialogue policy to a more natural and challenging setting, i.e., interacting dynamically with users. Further, we call attention to the non-cooperative user behavior - the user talks about off-path topics when he/she is not satisfied with the previous topics introduced by the agent. We argue that the targets of reaching the goal topic quickly and maintaining a high user satisfaction are not always converged, because the topics close to the goal and the topics user preferred may not be the same. Towards this issue, we propose a new solution named I-Pro that can learn Proactive policy in the Interactive setting. Specifically, we learn the trade-off via a learned goal weight, which consists of four factors (dialogue turn, goal completion difficulty, user satisfaction estimation, and cooperative degree). The experimental results demonstrate I-Pro significantly outperforms baselines in terms of effectiveness and interpretability.

Developing link prediction models to automatically complete knowledge graphs has recently been the focus of significant research interest. The current methods for the link prediction task have two natural problems: 1) the relation distributions in KGs are usually unbalanced, and 2) there are many unseen relations that occur in practical situations. These two problems limit the training effectiveness and practical applications of the existing link prediction models. We advocate a holistic understanding of KGs and we propose in this work a unified Generalized Relation Learning framework GRL to address the above two problems, which can be plugged into existing link prediction models. GRL conducts a generalized relation learning, which is aware of semantic correlations between relations that serve as a bridge to connect semantically similar relations. After training with GRL, the closeness of semantically similar relations in vector space and the discrimination of dissimilar relations are improved. We perform comprehensive experiments on six benchmarks to demonstrate the superior capability of GRL in the link prediction task. In particular, GRL is found to enhance the existing link prediction models making them insensitive to unbalanced relation distributions and capable of learning unseen relations.

Knowledge graphs are essential for numerous downstream natural language processing applications, but are typically incomplete with many facts missing.This results in research efforts on multi-hop reasoning task, which can be formulated as a search process and current models typically perform short distance reasoning.However, the long-distance reasoning is also vital with the ability to connect the superficially unrelated entities.To the best of our knowledge, there lacks a general framework that approaches multi-hop reasoning in mixed long-short distance reasoning scenarios.We argue that there are two key issues for a general multi-hop reasoning model: i) where to go, and ii) when to stop.Therefore, we propose a general model which resolves the issues with three modules: 1) the local-global knowledge module to estimate the possible paths, 2) the differentiated action dropout module to explore a diverse set of paths, and 3) the adaptive stopping search module to avoid over searching.The comprehensive results on three datasets demonstrate the superiority of our model with significant improvements against baselines in both short and long distance reasoning scenarios.

Definitive embeddings remain a fundamental challenge of computational musicology for symbolic music in deep learning today. Analogous to natural language, music can be modeled as a sequence of tokens. This motivates the majority of existing solutions to explore the utilization of word embedding models to build music embeddings. However, music differs from natural languages in two key aspects: (1) musical token is multi-faceted -- it comprises of pitch, rhythm and dynamics information; and (2) musical context is two-dimensional -- each musical token is dependent on both melodic and harmonic contexts. In this work, we provide a comprehensive solution by proposing a novel framework named PiRhDy that integrates pitch, rhythm, and dynamics information seamlessly. PiRhDy adopts a hierarchical strategy which can be decomposed into two steps: (1) token (i.e., note event) modeling, which separately represents pitch, rhythm, and dynamics and integrates them into a single token embedding; and (2) context modeling, which utilizes melodic and harmonic knowledge to train the token embedding. A thorough study was made on each component and sub-strategy of PiRhDy.We further validate our embeddings in three downstream tasks -- melody completion, accompaniment suggestion, and genre classification. Results indicate a significant advancement of the neural approach towards symbolic music as well as PiRhDy's potential as a pretrained tool for a broad range of symbolic music applications.

Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others. In contrast with previous works that focus mainly on single modal or bi-modal learning, we propose to learn social media content by fusing jointly textual, acoustic, and visual information (JTAV). Effective strategies are proposed to extract fine-grained features of each modality, that is, attBiGRU and DCRNN. We also introduce cross-modal fusion and attentive pooling techniques to integrate multi-modal information comprehensively. Extensive experimental evaluation conducted on real-world datasets demonstrate our proposed model outperforms the state-of-the-art approaches by a large margin.