pwc-archive/papers-with-abstracts huggingface dataset:
Evaluating Task-oriented Dialogue Systems: A Systematic Review of Measures, Constructs and their Operationalisations
Authors: Anouck Braggaar, Christine Liebrecht, Emiel van Miltenburg, Emiel Krahmer
Date: 2023-12-21 00:00:00
Tasks: Task-Oriented Dialogue Systems

This review gives an extensive overview of evaluation methods for task-oriented dialogue systems, paying special attention to practical applications of dialogue systems, for example for customer service. The review (1) provides an overview of the used constructs and metrics in previous work, (2) discusses challenges in the context of dialogue system evaluation and (3) develops a research agenda for the future of dialogue system evaluation. We conducted a systematic review of four databases (ACL, ACM, IEEE and Web of Science), which after screening resulted in 122 studies. Those studies were carefully analysed for the constructs and methods they proposed for evaluation. We found a wide variety in both constructs and methods. Especially the operationalisation is not always clearly reported. Newer developments concerning large language models are discussed in two contexts: to power dialogue systems and to use in the evaluation process. We hope that future work will take a more critical approach to the operationalisation and specification of the used constructs. To work towards this aim, this review ends with recommendations for evaluation and suggestions for outstanding questions.
Exploiting Contextual Target Attributes for Target Sentiment Classification
Authors: Bowen Xing, Ivor W. Tsang
Date: 2023-12-21 00:00:00
Tasks: Attribute, Classification, Cloze Test, Language Modeling, Language Modelling, Sentiment Analysis, Sentiment Classification

Existing PTLM-based models for TSC can be categorized into two groups: 1) fine-tuning-based models that adopt PTLM as the context encoder; 2) prompting-based models that transfer the classification task to the text/word generation task. In this paper, we present a new perspective of leveraging PTLM for TSC: simultaneously leveraging the merits of both language modeling and explicit target-context interactions via contextual target attributes. Specifically, we design the domain- and target-constrained cloze test, which can leverage the PTLMs' strong language modeling ability to generate the given target's attributes pertaining to the review context. The attributes contain the background and property information of the target, which can help to enrich the semantics of the review context and the target. To exploit the attributes for tackling TSC, we first construct a heterogeneous information graph by treating the attributes as nodes and combining them with (1) the syntax graph automatically produced by the off-the-shelf dependency parser and (2) the semantics graph of the review context, which is derived from the self-attention mechanism. Then we propose a heterogeneous information gated graph convolutional network to model the interactions among the attribute information, the syntactic information, and the contextual information. The experimental results on three benchmark datasets demonstrate the superiority of our model, which achieves new state-of-the-art performance.
Data Transformation to Construct a Dataset for Generating Entity-Relationship Model from Natural Language
Authors: Zhenwen Li, Jian-Guang Lou, Tao Xie
Date: 2023-12-21 00:00:00
Tasks: Text to SQL, Text-To-SQL

In order to reduce the manual cost of designing ER models, recent approaches have been proposed to address the task of NL2ERM, i.e., automatically generating entity-relationship (ER) models from natural language (NL) utterances such as software requirements. These approaches are typically rule-based ones, which rely on rigid heuristic rules; these approaches cannot generalize well to various linguistic ways of describing the same requirement. Despite having better generalization capability than rule-based approaches, deep-learning-based models are lacking for NL2ERM due to lacking a large-scale dataset. To address this issue, in this paper, we report our insight that there exists a high similarity between the task of NL2ERM and the increasingly popular task of text-to-SQL, and propose a data transformation algorithm that transforms the existing data of text-to-SQL into the data of NL2ERM. We apply our data transformation algorithm on Spider, one of the most popular text-to-SQL datasets, and we also collect some data entries with different NL types, to obtain a large-scale NL2ERM dataset. Because NL2ERM can be seen as a special information extraction (IE) task, we train two state-of-the-art IE models on our dataset. The experimental results show that both the two models achieve high performance and outperform existing baselines.
Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation
Authors: Jiayu Lin, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Zhongyu Wei
Date: 2023-12-21 00:00:00
Tasks: Sentence

Counter-argument generation -- a captivating area in computational linguistics -- seeks to craft statements that offer opposing views. While most research has ventured into paragraph-level generation, sentence-level counter-argument generation beckons with its unique constraints and brevity-focused challenges. Furthermore, the diverse nature of counter-arguments poses challenges for evaluating model performance solely based on n-gram-based metrics. In this paper, we present the ArgTersely benchmark for sentence-level counter-argument generation, drawing from a manually annotated dataset from the ChangeMyView debate forum. We also propose Arg-LlaMA for generating high-quality counter-argument. For better evaluation, we trained a BERT-based evaluator Arg-Judge with human preference data. We conducted comparative experiments involving various baselines such as LlaMA, Alpaca, GPT-3, and others. The results show the competitiveness of our proposed framework and evaluator in counter-argument generation tasks. Code and data are available at https://github.com/amazingljy1206/ArgTersely.
Speech Translation with Large Language Models: An Industrial Practice
Authors: Zhichao Huang, Rong Ye, Tom Ko, Qianqian Dong, Shanbo Cheng, Mingxuan Wang, Hang Li
Date: 2023-12-21 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Translation

Given the great success of large language models (LLMs) across various tasks, in this paper, we introduce LLM-ST, a novel and effective speech translation model constructed upon a pre-trained LLM. By integrating the large language model (LLM) with a speech encoder and employing multi-task instruction tuning, LLM-ST can produce accurate timestamped transcriptions and translations, even from long audio inputs. Furthermore, our findings indicate that the implementation of Chain-of-Thought (CoT) prompting can yield advantages in the context of LLM-ST. Through rigorous experimentation on English and Chinese datasets, we showcase the exceptional performance of LLM-ST, establishing a new benchmark in the field of speech translation. Demo: https://speechtranslation.github.io/llm-st/.
How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry'' Benchmark
Authors: Eldar Kurtic, Torsten Hoefler, Dan Alistarh
Date: 2023-12-21 00:00:00
Tasks: Knowledge Distillation, Language Modeling, Language Modelling

Pruning large language models (LLMs) from the BERT family has emerged as a standard compression benchmark, and several pruning methods have been proposed for this task. The recent ``Sparsity May Cry'' (SMC) benchmark put into question the validity of all existing methods, exhibiting a more complex setup where many known pruning methods appear to fail. We revisit the question of accurate BERT-pruning during fine-tuning on downstream datasets, and propose a set of general guidelines for successful pruning, even on the challenging SMC benchmark. First, we perform a cost-vs-benefits analysis of pruning model components, such as the embeddings and the classification head; second, we provide a simple-yet-general way of scaling training, sparsification and learning rate schedules relative to the desired target sparsity; finally, we investigate the importance of proper parametrization for Knowledge Distillation in the context of LLMs. Our simple insights lead to state-of-the-art results, both on classic BERT-pruning benchmarks, as well as on the SMC benchmark, showing that even classic gradual magnitude pruning (GMP) can yield competitive results, with the right approach.
Developing Interactive Tourism Planning: A Dialogue Robot System Powered by a Large Language Model
Authors: Katsumasa Yoshikawa, Takato Yamazaki, Masaya Ohagi, Tomoya Mizumoto, Keiya Sato
Date: 2023-12-21 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

In recent years, large language models (LLMs) have rapidly proliferated and have been utilized in various tasks, including research in dialogue systems. We aimed to construct a system that not only leverages the flexible conversational abilities of LLMs but also their advanced planning capabilities to reduce the speaking load on human interlocutors and efficiently plan trips. Furthermore, we propose a method that divides the complex task of a travel agency into multiple subtasks, managing each as a separate phase to effectively accomplish the task. Our proposed system confirmed a certain level of success by achieving fourth place in the Dialogue Robot Competition 2023 preliminaries rounds. We report on the challenges identified through the competition.
Automated Clinical Coding for Outpatient Departments
Authors: Viktor Schlegel, Abhinav Ramesh Kashyap, Thanh-Tung Nguyen, Tsung-Han Yang, Vijay Prakash Dwivedi, Wei-Hsian Yin, Jeng Wei, Stefan Winkler
Date: 2023-12-21 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Computerised clinical coding approaches aim to automate the process of assigning a set of codes to medical records. While there is active research pushing the state of the art on clinical coding for hospitalized patients, the outpatient setting -- where doctors tend to non-hospitalised patients -- is overlooked. Although both settings can be formalised as a multi-label classification task, they present unique and distinct challenges, which raises the question of whether the success of inpatient clinical coding approaches translates to the outpatient setting. This paper is the first to investigate how well state-of-the-art deep learning-based clinical coding approaches work in the outpatient setting at hospital scale. To this end, we collect a large outpatient dataset comprising over 7 million notes documenting over half a million patients. We adapt four state-of-the-art clinical coding approaches to this setting and evaluate their potential to assist coders. We find evidence that clinical coding in outpatient settings can benefit from more innovations in popular inpatient coding benchmarks. A deeper analysis of the factors contributing to the success -- amount and form of data and choice of document representation -- reveals the presence of easy-to-solve examples, the coding of which can be completely automated with a low error rate.
VADIS -- a VAriable Detection, Interlinking and Summarization system
Authors: Yavuz Selim Kartal, Muhammad Ahsan Shahid, Sotaro Takeshita, Tornike Tsereteli, Andrea Zielinski, Benjamin Zapilko, Philipp Mayr
Date: 2023-12-20 00:00:00
Tasks: Survey, Variable Detection

The VADIS system addresses the demand of providing enhanced information access in the domain of the social sciences. This is achieved by allowing users to search and use survey variables in context of their underlying research data and scholarly publications which have been interlinked with each other.
Time is Encoded in the Weights of Finetuned Language Models
Authors: Kai Nylund, Suchin Gururangan, Noah A. Smith
Date: 2023-12-20 00:00:00
Tasks: Language Modeling, Language Modelling

We present time vectors, a simple tool to customize language models to new time periods. Time vectors are created by finetuning a language model on data from a single time (e.g., a year or month), and then subtracting the weights of the original pretrained model. This vector specifies a direction in weight space that, as our experiments show, improves performance on text from that time period. Time vectors specialized to adjacent time periods appear to be positioned closer together in a manifold. Using this structure, we interpolate between time vectors to induce new models that perform better on intervening and future time periods, without any additional training. We demonstrate the consistency of our findings across different tasks, domains, model sizes, and time scales. Our results suggest that time is encoded in the weight space of finetuned models.
Benchmarking Evolutionary Community Detection Algorithms in Dynamic Networks
Authors: Giordano Paoletti, Luca Gioacchini, Marco Mellia, Luca Vassio, Jussara M. Almeida
Date: 2023-12-21 00:00:00
Tasks: Benchmarking, Community Detection

In dynamic complex networks, entities interact and form network communities that evolve over time. Among the many static Community Detection (CD) solutions, the modularity-based Louvain, or Greedy Modularity Algorithm (GMA), is widely employed in real-world applications due to its intuitiveness and scalability. Nevertheless, addressing CD in dynamic graphs remains an open problem, since the evolution of the network connections may poison the identification of communities, which may be evolving at a slower pace. Hence, naively applying GMA to successive network snapshots may lead to temporal inconsistencies in the communities. Two evolutionary adaptations of GMA, sGMA and $\alpha$GMA, have been proposed to tackle this problem. Yet, evaluating the performance of these methods and understanding to which scenarios each one is better suited is challenging because of the lack of a comprehensive set of metrics and a consistent ground truth. To address these challenges, we propose (i) a benchmarking framework for evolutionary CD algorithms in dynamic networks and (ii) a generalised modularity-based approach (NeGMA). Our framework allows us to generate synthetic community-structured graphs and design evolving scenarios with nine basic graph transformations occurring at different rates. We evaluate performance through three metrics we define, i.e. Correctness, Delay, and Stability. Our findings reveal that $\alpha$GMA is well-suited for detecting intermittent transformations, but struggles with abrupt changes; sGMA achieves superior stability, but fails to detect emerging communities; and NeGMA appears a well-balanced solution, excelling in responsiveness and instantaneous transformations detection.
Trochoid Search Optimization
Authors: Abdesslem Layeb
Date: 2023-12-21 00:00:00

This paper introduces the Trochoid Search Optimization Algorithm (TSO), a novel metaheuristic leveraging the mathematical properties of trochoid curves. The TSO algorithm employs a unique combination of simultaneous translational and rotational motions inherent in trochoids, fostering a refined equilibrium between explorative and exploitative search capabilities. Notably, TSO consists of two pivotal phases global and local search that collectively contribute to its efficiency and efficacy. Experimental validation demonstrates the TSO algorithm's remarkable performance across various benchmark functions, showcasing its competitive edge in balancing exploration and exploitation within the search space. A distinguishing feature of TSO lies in its simplicity, marked by a minimal requirement for user-defined parameters, making it an accessible yet powerful optimization tool.
Enhancing Optimization Through Innovation: The Multi-Strategy Improved Black Widow Optimization Algorithm (MSBWOA)
Authors: Xin Xu
Date: 2023-12-20 00:00:00
Tasks: Diversity

This paper introduces a Multi-Strategy Improved Black Widow Optimization Algorithm (MSBWOA), designed to enhance the performance of the standard Black Widow Algorithm (BW) in solving complex optimization problems. The proposed algorithm integrates four key strategies: initializing the population using Tent chaotic mapping to enhance diversity and initial exploratory capability; implementing mutation optimization on the least fit individuals to maintain dynamic population and prevent premature convergence; incorporating a non-linear inertia weight to balance global exploration and local exploitation; and adding a random perturbation strategy to enhance the algorithm's ability to escape local optima. Evaluated through a series of standard test functions, the MSBWOA demonstrates significant performance improvements in various dimensions, particularly in convergence speed and solution quality. Experimental results show that compared to the traditional BW algorithm and other existing optimization methods, the MSBWOA exhibits better stability and efficiency in handling a variety of optimization problems. These findings validate the effectiveness of the proposed strategies and offer a new solution approach for complex optimization challenges.
Unexplored Frontiers: A Review of Empirical Studies of Exploratory Search
Authors: Alan Medlar, Denis Kotkov, Dorota Glowacka
Date: 2023-12-21 00:00:00
Tasks: Articles, Retrieval

This article reviews how empirical research of exploratory search is conducted. We investigated aspects of interdisciplinarity, study settings and evaluation methodologies from a systematically selected sample of 231 publications from 2010-2021, including a total of 172 articles with empirical studies. Our results show that exploratory search is highly interdisciplinary, with the most frequently occurring publication venues including high impact venues in information science, information systems and human-computer interaction. However, taken in aggregate, the breadth of study settings investigated was limited. We found that a majority of studies (77%) focused on evaluating novel retrieval systems as opposed to investigating users' search processes. Furthermore, a disproportionate number of studies were based on scientific literature search (20.7%), a majority of which only considered searching for Computer Science articles. Study participants were generally from convenience samples, with 75% of studies composed exclusively of students and other academics. The methodologies used for evaluation were mostly quantitative, but lacked consistency between studies and validated questionnaires were rarely used. In discussion, we offer a critical analysis of our findings and suggest potential improvements for future exploratory search studies.
Aligning Language Models with Human Preferences via a Bayesian Approach
Authors: Jiashuo Wang, Haozhao Wang, Shichao Sun, Wenjie Li
Date: 2023-10-09 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Contrastive Learning, Reinforcement Learning (RL), Text Generation

In the quest to advance human-centric natural language generation (NLG) systems, ensuring alignment between NLG models and human preferences is crucial. For this alignment, current popular methods leverage a reinforcement learning (RL) approach with a reward model trained on feedback from humans. However, inherent disagreements due to the subjective nature of human preferences pose a significant challenge for training the reward model, resulting in a deterioration of the NLG performance. To tackle this issue, previous approaches typically rely on majority voting or averaging to consolidate multiple inconsistent preferences into a merged one. Although straightforward to understand and execute, such methods suffer from an inability to capture the nuanced degrees of disaggregation among humans and may only represent a specialized subset of individuals, thereby lacking the ability to quantitatively disclose the universality of human preferences. To address this challenge, this paper proposes a novel approach, which employs a Bayesian framework to account for the distribution of disagreements among human preferences as training a preference model, and names it as d-PM. Besides, considering the RL strategy's inefficient and complex training process over the training efficiency, we further propose utilizing the contrastive learning strategy to train the NLG model with the preference scores derived from the d-PM model. Extensive experiments on two human-centric NLG tasks, i.e., emotional support conversation and integrity "Rule-of-Thumb" generation, show that our method consistently exceeds previous SOTA models in both automatic and human evaluations.
Decision-Making Frameworks for Network Resilience -- Managing and Mitigating Systemic (Cyber) Risk
Authors: Gregor Svindland, Alexander Voß
Date: 2023-12-21 00:00:00
Tasks: Decision Making, Management

We introduce a decision-making framework tailored for the management of systemic risk in networks. This framework is constructed upon three fundamental components: (1) a set of acceptable network configurations, (2) a set of interventions aimed at risk mitigation, and (3) a cost function quantifying the expenses associated with these interventions. While our discussion primarily revolves around the management of systemic cyber risks in digital networks, we concurrently draw parallels to risk management of other complex systems where analogous approaches may be adequate.
Responsible Deep Learning for Software as a Medical Device
Authors: Pratik Shah, Jenna Lester, Jana G Deflino, Vinay Pai
Date: 2023-12-20 00:00:00
Tasks: Deep Learning, Medical Image Analysis

Tools, models and statistical methods for signal processing and medical image analysis and training deep learning models to create research prototypes for eventual clinical applications are of special interest to the biomedical imaging community. But material and optical properties of biological tissues are complex and not easily captured by imaging devices. Added complexity can be introduced by datasets with underrepresentation of medical images from races and ethnicities for deep learning, and limited knowledge about the regulatory framework needed for commercialization and safety of emerging Artificial Intelligence (AI) and Machine Learning (ML) technologies for medical image analysis. This extended version of the workshop paper presented at the special session of the 2022 IEEE 19th International Symposium on Biomedical Imaging, describes strategy and opportunities by University of California professors engaged in machine learning (section I) and clinical research (section II), the Office of Science and Engineering Laboratories (OSEL) section III, and officials at the US FDA in Center for Devices & Radiological Health (CDRH) section IV. Performance evaluations of AI/ML models of skin (RGB), tissue biopsy (digital pathology), and lungs and kidneys (Magnetic Resonance, X-ray, Computed Tomography) medical images for regulatory evaluations and real-world deployment are discussed.