GenAI4ELab/papercli-papers huggingface dataset:
PasCore: A Chinese Overlapping Relation Extraction Model Based on Global Pointer Annotation Strategy
Authors: Peng Wang; Jiafeng Xie; Xiye Chen; Guozheng Li; Wei Li
Venue: IJCAI 2023 (main)

Abstract: Recent work for extracting relations from texts has achieved excellent performance. However, existing studies mainly focus on simple relation extraction, these methods perform not well on overlapping triple problem because the tags of shared entities would conflict with each other. Especially, overlapping entities are common and indispensable in Chinese. To address this issue, this paper proposes PasCore, which utilizes a global pointer annotation strategy for overlapping relation extraction in Chinese. PasCore first obtains the sentence vector via general pre-training model encoder, and uses classifier to predicate relations. Subsequently, it uses global pointer annotation strategy for head entity annotation, which uses global tags to label the start and end positions of the entities. Finally, PasCore integrates the relation, head entity and its type to mark the tail entity. Furthermore, PasCore performs conditional layer normalization to fuse features, which connects all stages and greatly enriches the association between relations and entities. Experimental results on both Chinese and English real-world datasets demonstrate that PasCore outperforms strong baselines on relation extraction and, especially, shows superior performance on overlapping relation extraction.
Privacy-Preserving End-to-End Spoken Language Understanding
Authors: Yinggui Wang; Wei Huang; Le Yang
Venue: IJCAI 2023 (main)

Abstract: Spoken language understanding (SLU), one of the key enabling technologies for human-computer interaction in IoT devices, provides an easy-to-use user interface. Human speech can contain a lot of user-sensitive information, such as gender, identity, and sensitive content. New types of security and privacy breaches have thus emerged. Users do not want to expose their personal sensitive information to malicious attacks by untrusted third parties. Thus, the SLU system needs to ensure that a potential malicious attacker cannot deduce the sensitive attributes of the users, while it should avoid greatly compromising the SLU accuracy. To address the above challenge, this paper proposes a novel SLU multi-task privacy-preserving model to prevent both the speech recognition (ASR) and identity recognition (IR) attacks. The model uses the hidden layer separation technique so that SLU information is distributed only in a specific portion of the hidden layer, and the other two types of information are removed to obtain a privacy-secure hidden layer. In order to achieve good balance between efficiency and privacy, we introduce a new mechanism of model pre-training, namely joint adversarial training, to further enhance the user privacy. Experiments over two SLU datasets show that the proposed method can reduce the accuracy of both the ASR and IR attacks close to that of a random guess, while leaving the SLU performance largely unaffected.
Beyond Pure Text: Summarizing Financial Reports Based on Both Textual and Tabular Data
Authors: Ziao Wang; Zelin Jiang; Xiaofeng Zhang; Jaehyeon Soon; Jialu Zhang; Wang Xiaoyao; Hongwei Du
Venue: IJCAI 2023 (main)

Abstract: Abstractive text summarization is to generate concise summaries that well preserve both salient information and the overall semantic meanings of the given documents. However, real-world documents, e.g., financial reports, generally contain rich data such as charts and tabular data which invalidates most existing text summarization approaches. This paper is thus motivated to propose this novel approach to simultaneously summarize both textual and tabular data. Particularly, we first manually construct a “table+text → summary” dataset. Then, the tabular data is respectively embedded in a row-wise and column-wise manner, and the textual data is encoded at the sentence-level via an employed pre-trained model. We propose a salient detector gate respectively performed between each pair of row/column and sentence embeddings. The highly correlated content is considered as salient information that must be summarized. Extensive experiments have been performed on our constructed dataset and the promising results demonstrate the effectiveness of the proposed approach w.r.t. a number of both automatic and human evaluation criteria.
Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video
Authors: Zenan Xu; Xiaojun Meng; Yasheng Wang; Qinliang Su; Zexuan Qiu; Xin Jiang; Qun Liu
Venue: IJCAI 2023 (main)

Abstract: Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our case, the video content and its transcript. Inspired by the success of the large-scale generative pre-trained language model (GPLM) in generating high-quality textual content (e.g., summary), recent MAS methods have proposed to adapt the GPLM to this task by equipping it with the visual information, which is often obtained through a general-purpose visual feature extractor. However, the generally extracted visual features may overlook some summary-worthy visual information, which impedes model performance. In this work, we propose a novel approach to learning the summary-worthy visual representation that facilitates abstractive summarization. Our method exploits the summary-worthy information from both the cross-modal transcript data and the knowledge that distills from the pseudo summary. Extensive experiments on three public multimodal datasets show that our method outperforms all competing baselines. Furthermore, with the advantages of summary-worthy visual information, our model can have a significant improvement on small datasets or even datasets with limited training data.
TITAN : Task-oriented Dialogues with Mixed-Initiative Interactions
Authors: Sitong Yan; Shengli Song; Jingyang Li; Shiqi Meng; Guangneng Hu
Venue: IJCAI 2023 (main)

Abstract: In multi-domain task-oriented dialogue systems, users proactively propose a series of domain-specific requests that can often be under-or over-specified, sometimes with ambiguous and cross-domain demands. System-sided initiative would be necessary to identify certain situations and appropriately interact with users to resolve them. However, most existing task-oriented dialogue systems fail to consider such mixed-initiative interaction strategies, performing low efficiency and poor collaboration ability in human-computer conversation. In this paper, we construct a multi-domain task-oriented dialogue dataset with mixed-initiative strategies named TITAN from the large-scale dialogue corpus MultiWOZ 2.1. It contains a total of 1,800 human-human conversations where the system can either ask clarification questions actively or provides relevant information to address failure situations and implicit user requests. We report the results of several baseline models on system response generation and dialogue act prediction to assess the performance of SOTA methods on TITAN. These models can capture mixed-initiative dialogue acts, while remaining the deficiency to actively generate implicit requests and accurately provide alternative information, suggesting ample room for improvement in future studies.
Efficient Sign Language Translation with a Curriculum-based Non-autoregressive Decoder
Authors: Pei Yu; Liang Zhang; Biao Fu; Yidong Chen
Venue: IJCAI 2023 (main)

Abstract: Most existing studies on Sign Language Translation (SLT) employ AutoRegressive Decoding Mechanism (AR-DM) to generate target sentences. However, the main disadvantage of the AR-DM is high inference latency. To address this problem, we introduce Non-AutoRegressive Decoding Mechanism (NAR-DM) into SLT, which generates the whole sentence at once. Meanwhile, to improve its decoding ability, we integrate the advantages of curriculum learning and NAR-DM and propose a Curriculum-based NAR Decoder (CND). Specifically, the lower layers of the CND are expected to predict simple tokens that could be predicted correctly using source-side information solely. Meanwhile, the upper layers could predict complex tokens based on the lower layers' predictions. Therefore, our CND significantly reduces the model's inference latency while maintaining its competitive performance. Moreover, to further boost the performance of our CND, we propose a mutual learning framework, containing two decoders, i.e., an AR decoder and our CND. We jointly train the two decoders and minimize the KL divergence between their outputs, which enables our CND to learn the forward sequential knowledge from the strengthened AR decoder. Experimental results on PHOENIX2014T and CSL-Daily demonstrate that our model consistently outperforms all competitive baselines and achieves 7.92/8.02× speed-up compared to the AR SLT model respectively. Our source code is available at https://github.com/yp20000921/CND.
Fast-StrucTexT: An Efficient Hourglass Transformer with Modality-guided Dynamic Token Merge for Document Understanding
Authors: Mingliang Zhai; Yulin Li; Xiameng Qin; Chen Yi; Qunyi Xie; Chengquan Zhang; Kun Yao; Yuwei Wu; Yunde Jia
Venue: IJCAI 2023 (main)

Abstract: Transformers achieve promising performance in document understanding because of their high effectiveness and still suffer from quadratic computational complexity dependency on the sequence length. General efficient transformers are challenging to be directly adapted to model document. They are unable to handle the layout representation in documents, e.g. word, line and paragraph, on different granularity levels and seem hard to achieve a good trade-off between efficiency and performance. To tackle the concerns, we propose Fast-StrucTexT, an efficient multi-modal framework based on the StrucTexT algorithm with an hourglass transformer architecture, for visual document understanding. Specifically, we design a modality-guided dynamic token merging block to make the model learn multi-granularity representation and prunes redundant tokens. Additionally, we present a multi-modal interaction module called Symmetry Cross-Attention (SCA) to consider multi-modal fusion and efficiently guide the token mergence. The SCA allows one modality input as query to calculate cross attention with another modality in a dual phase. Extensive experiments on FUNSD, SROIE, and CORD datasets demonstrate that our model achieves the state-of-the-art performance and almost 1.9x faster inference time than the state-of-the-art methods.
Exploring Effective Inter-Encoder Semantic Interaction for Document-Level Relation Extraction
Authors: Liang Zhang; Zijun Min; Jinsong Su; Pei Yu; Ante Wang; Yidong Chen
Venue: IJCAI 2023 (main)

Abstract: In document-level relation extraction (RE), the models are required to correctly predict implicit relations in documents via relational reasoning. To this end, many graph-based methods have been proposed for this task. Despite their success, these methods still suffer from several drawbacks: 1) their interaction between document encoder and graph encoder is usually unidirectional and insufficient; 2) their graph encoders often fail to capture the global context of nodes in document graph. In this paper, we propose a document-level RE model with a Graph-Transformer Network (GTN). The GTN includes two core sublayers: 1) the graph-attention sublayer that simultaneously models global and local contexts of nodes in the document graph; 2) the cross-attention sublayer, enabling GTN to capture the non-entity clue information from the document encoder. Furthermore, we introduce two auxiliary training tasks to enhance the bidirectional semantic interaction between the document encoder and GTN: 1) the graph node reconstruction that can effectively train our cross-attention sublayer to enhance the semantic transition from the document encoder to GTN; 2) the structure-aware adversarial knowledge distillation, by which we can effectively transfer the structural information of GTN to the document encoder. Experimental results on four benchmark datasets prove the effectiveness of our model. Our source code is available at https://github.com/DeepLearnXMU/DocRE-BSI.
NerCo: A Contrastive Learning Based Two-Stage Chinese NER Method
Authors: Zai Zhang; Bin Shi; Haokun Zhang; Huang Xu; Yaodong Zhang; Yuefei Wu; Bo Dong; Qinghua Zheng
Venue: IJCAI 2023 (main)

Abstract: Sequence labeling serves as the most commonly used scheme for Chinese named entity recognition(NER). However, traditional sequence labeling methods classify tokens within an entity into different classes according to their positions. As a result, different tokens in the same entity may be learned with representations that are isolated and unrelated in target representation space, which could finally negatively affect the subsequent performance of token classification. In this paper, we point out and define this problem as Entity Representation Segmentation in Label-semantics. And then we present NerCo: Named entity recognition with Contrastive learning, a novel NER framework which can better exploit labeled data and avoid the above problem. Following the pretrain-finetune paradigm, NerCo firstly guides the encoder to learn powerful label-semantics based representations by gathering the encoded token representations of the same Semantic Class while pushing apart that of different. Subsequently, NerCo finetunes the learned encoder for final entity prediction. Extensive experiments on several datasets demonstrate that our framework can consistently improve the baseline and achieve state-of-the-art performance.
Genetic Prompt Search via Exploiting Language Model Probabilities
Authors: Jiangjiang Zhao; Zhuoran Wang; Fangchun Yang
Venue: IJCAI 2023 (main)

Abstract: Prompt tuning for large-scale pretrained language models (PLMs) has shown remarkable potential, especially in low-resource scenarios such as few-shot learning. Moreover, derivative-free optimisation (DFO) techniques make it possible to tune prompts for a black-box PLM to better fit downstream tasks. However, there are usually preconditions to apply existing DFO-based prompt tuning methods, e.g. the backbone PLM needs to provide extra APIs so that hidden states (and/or embedding vectors) can be injected into it as continuous prompts, or carefully designed (discrete) manual prompts need to be available beforehand, serving as the initial states of the tuning algorithm. To waive such preconditions and make DFO-based prompt tuning ready for general use, this paper introduces a novel genetic algorithm (GA) that evolves from empty prompts, and uses the predictive probabilities derived from the backbone PLM(s) on the basis of a (few-shot) training set to guide the token selection process during prompt mutations. Experimental results on diverse benchmark datasets show that the proposed precondition-free method significantly outperforms the existing DFO-style counterparts that require preconditions, including black-box tuning, genetic prompt search and gradient-free instructional prompt search.
Learning Few-shot Sample-set Operations for Noisy Multi-label Aspect Category Detection
Authors: Shiman Zhao; Wei Chen; Tengjiao Wang
Venue: IJCAI 2023 (main)

Abstract: Multi-label Aspect Category Detection (MACD) is essential for aspect-based sentiment analysis, which aims to identify multiple aspect categories in a given sentence. Few-shot MACD is critical due to the scarcity of labeled data. However, MACD is a high-noise task, and existing methods fail to address it with only two or three training samples per class, which limits the application in practice. To solve above issues, we propose a group of Few-shot Sample-set Operations (FSO) to solve noisy MACD in fewer sample scenarios by identifying the semantic contents of samples. Learning interactions among intersection, subtraction, and union networks, the FSO imitates arithmetic operations on samples to distinguish relevant and irrelevant aspect contents. Eliminating the negative effect caused by noises, the FSO extracts discriminative prototypes and customizes a dedicated query vector for each class. Besides, we design a multi-label architecture, which integrates with score-wise loss and multi-label loss to optimize the FSO for multi-label prediction, avoiding complex threshold training or selection. Experiments show that our method achieves considerable performance. Significantly, it improves by 11.01% at most and an average of 8.59% Macro-F in fewer sample scenarios.
COOL, a Context Outlooker, and Its Application to Question Answering and Other Natural Language Processing Tasks
Authors: Fangyi Zhu; See-Kiong Ng; Stéphane Bressan
Venue: IJCAI 2023 (main)

Abstract: Vision outlooker improves the performance of vision transformers, which implements a self-attention mechanism by adding an outlook attention, a form of local attention. In natural language processing, as has been the case in computer vision and other domains, transformer-based models constitute the state-of-the-art for most processing tasks. In this domain, too, many authors have argued and demonstrated the importance of local context. We present an outlook attention mechanism, COOL, for natural language processing. COOL, added on top of the self-attention layers of a transformer-based model, encodes local syntactic context considering word proximity and more pair-wise constraints than dynamic convolution used by existing approaches. A comparative empirical performance evaluation of an implementation of COOL with different transformer-based models confirms the opportunity for improvement over a baseline using the original models alone for various natural language processing tasks, including question answering. The proposed approach achieves competitive performance with existing state-of-the-art methods on some tasks.
DiSProD: Differentiable Symbolic Propagation of Distributions for Planning
Authors: Palash Chatterjee; Ashutosh Chapagain; Weizhe Chen; Roni Khardon
Venue: IJCAI 2023 (main)

Abstract: The paper introduces DiSProD, an online planner developed for environments with probabilistic transitions in continuous state and action spaces. DiSProD builds a symbolic graph that captures the distribution of future trajectories, conditioned on a given policy, using independence assumptions and approximate propagation of distributions. The symbolic graph provides a differentiable representation of the policy's value, enabling efficient gradient-based optimization for long-horizon search. The propagation of approximate distributions can be seen as an aggregation of many trajectories, making it well-suited for dealing with sparse rewards and stochastic environments. An extensive experimental evaluation compares DiSProD to state-of-the-art planners in discrete-time planning and real-time control of robotic systems. The proposed method improves over existing planners in handling stochastic environments, sensitivity to search depth, sparsity of rewards, and large action spaces. Additional real-world experiments demonstrate that DiSProD can control ground vehicles and surface vessels to successfully navigate around obstacles.
Minimizing Reachability Times on Temporal Graphs via Shifting Labels
Authors: Argyrios Deligkas; Eduard Eiben; George Skretas
Venue: IJCAI 2023 (main)

Abstract: We study how we can accelerate the spreading of information in temporal graphs via shifting operations; a problem that captures real-world applications varying from information flows to distribution schedules. In a temporal graph there is a set of fixed vertices and the available connections between them change over time in a predefined manner. We observe that, in some cases, shifting some connections, i.e., advancing or delaying them, can decrease the time required to reach from some vertex (source) to another vertex. We study how we can minimize the maximum time a set of sources needs to reach every vertex, when we are allowed to shift some of the connections. If we restrict the allowed number of changes, we prove that, already for a single source, the problem is NP-hard, and W[2]-hard when parameterized by the number of changes. Then we focus on unconstrained number of changes. We derive a polynomial-time algorithm when there is one source. When there are two sources, we show that the problem becomes NP-hard; on the other hand, we design an FPT algorithm parameterized by the treewidth of the graph plus the lifetime of the optimal solution, that works for any number of sources. Finally, we provide polynomial-time algorithms for several graph classes.
On the Compilability of Bounded Numeric Planning
Authors: Nicola Gigante; Enrico Scala
Venue: IJCAI 2023 (main)

Abstract: Bounded numeric planning, where each numeric variable domain is bounded, is PSPACE-complete, but such a complexity result does not capture how hard it really is, since the same holds even for the practically much easier STRIPS fragment. A finer way to compare the difficulty of planning formalisms is through the notion of compilability, which has been however extensively studied only for classical planning by Nebel. This paper extends Nebel's framework to the setting of bounded numeric planning. First, we identify a variety of numeric fragments differing on the degree of the polynomials involved and the availability of features such as conditional effects and Boolean conditions; then we study the compilability of these fragments to each other and to the classical fragments. Surprisingly, numeric and classical planning with conditional effects and Boolean conditions can be compiled both ways preserving plan size exactly, while the same does not hold when targeting pure STRIPS. Our study reveals also that numeric fragments cluster into two equivalence classes separated by the availability of incomplete initial state specifications, a feature allowing to specify uncertainty in the initial state.
On the Study of Curriculum Learning for Inferring Dispatching Policies on the Job Shop Scheduling
Authors: Zangir Iklassov; Dmitrii Medvedev; Ruben Solozabal Ochoa de Retana; Martin Takac
Venue: IJCAI 2023 (main)

Abstract: This paper studies the use of Curriculum Learning on Reinforcement Learning (RL) to improve the performance of the dispatching policies learned on the Job-shop Scheduling Problem (JSP). Current works in the literature present a large optimality gap when learning end-to-end solutions on this problem. In this regard, we identify the difficulty for RL to learn directly on large instances as part of the issue and use Curriculum Learning (CL) to mitigate this effect. Particularly, CL sequences the learning process in a curriculum of increasing complexity tasks, which allows learning on large instances that otherwise would be impossible to learn from scratch. In this paper, we present a size-agnostic model that enables us to demonstrate that current curriculum strategies have a major impact on the quality of the solution inferred. In addition, we introduce a novel Reinforced Adaptive Staircase Curriculum Learning (RASCL) strategy, which adjusts the difficulty level during the learning process by revisiting the worst-performing instances. Conducted experiments on Taillard’s and Demirkol’s datasets show that the presented approach significantly improves the current stateof-the-art models on the JSP. It reduces the average optimality gap from 19.35% to 10.46% on Taillard’s instances and from 38.43% to 18.85% on Demirkol’s instances.