pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Event Causality Identification with Rationale and Structure-Aware Causal Question Answering
Authors: Baiyan Zhang, Qin Chen, Jie zhou, Jian Jin, Liang He
Date: 2024-03-17 00:00:00
Tasks: Event Causality Identification, Multiple-choice, Multi-Task Learning, Question Answering

Document-level Event Causality Identification (DECI) aims to identify causal relations between two events in documents. Recent research tends to use pre-trained language models to generate the event causal relations. Whereas, these methods are prone to the errors of sequential generation due to multiple events in a document. Moreover, the potential structures such as event coreference and related causal chain are neglected. In this paper, we propose a multi-task learning framework to enhance event causality identification with rationale and structure-aware causal question answering. Specifically, the DECI task is transformed into multiple-choice question answering, and the causes and effects of the questioned event are generated with large language models. In addition, we generate the rationales to explain why these events have causal relations. Moreover, we construct an event structure graph, which models the multi-hop potential relations for causal reasoning of the current event. Experiments on two benchmark datasets show the great advantages of our proposed approach compared to the state-of-the-art methods. Moreover, we conduct both quantitative and qualitative analyses, which shed light on why each component of our approach can lead to great improvements.
Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants' API Invocation Capabilities
Authors: Honglin Mu, Yang Xu, Yunlong Feng, Xiaofeng Han, Yitong Li, Yutai Hou, Wanxiang Che
Date: 2024-03-17 00:00:00

With the rise of Large Language Models (LLMs), AI assistants' ability to utilize tools, especially through API calls, has advanced notably. This progress has necessitated more accurate evaluation methods. Many existing studies adopt static evaluation, where they assess AI assistants' API call based on pre-defined dialogue histories. However, such evaluation method can be misleading, as an AI assistant might fail in generating API calls from preceding human interaction in real cases. Instead of the resource-intensive method of direct human-machine interactions, we propose Automated Dynamic Evaluation (AutoDE) to assess an assistant's API call capability without human involvement. In our framework, we endeavor to closely mirror genuine human conversation patterns in human-machine interactions, using a LLM-based user agent, equipped with a user script to ensure human alignment. Experimental results highlight that AutoDE uncovers errors overlooked by static evaluations, aligning more closely with human assessment. Testing four AI assistants using our crafted benchmark, our method further mirrored human evaluation compared to conventional static evaluations.
Granular Change Accuracy: A More Accurate Performance Metric for Dialogue State Tracking
Authors: Taha Aksu, Nancy F. Chen
Date: 2024-03-17 00:00:00
Tasks: Benchmarking, Dialogue State Tracking

Current metrics for evaluating Dialogue State Tracking (DST) systems exhibit three primary limitations. They: i) erroneously presume a uniform distribution of slots throughout the dialog, ii) neglect to assign partial scores for individual turns, iii) frequently overestimate or underestimate performance by repeatedly counting the models' successful or failed predictions. To address these shortcomings, we introduce a novel metric: Granular Change Accuracy (GCA). GCA focuses on evaluating the predicted changes in dialogue state over the entire dialogue history. Benchmarking reveals that GCA effectively reduces biases arising from distribution uniformity and the positioning of errors across turns, resulting in a more precise evaluation. Notably, we find that these biases are particularly pronounced when evaluating few-shot or zero-shot trained models, becoming even more evident as the model's error rate increases. Hence, GCA offers significant promise, particularly for assessing models trained with limited resources. Our GCA implementation is a useful addition to the pool of DST metrics.
HarmPot: An Annotation Framework for Evaluating Offline Harm Potential of Social Media Text
Authors: Ritesh Kumar, Ojaswee Bhalla, Madhu Vanthi, Shehlat Maknoon Wani, Siddharth Singh
Date: 2024-03-17 00:00:00

In this paper, we discuss the development of an annotation schema to build datasets for evaluating the offline harm potential of social media texts. We define "harm potential" as the potential for an online public post to cause real-world physical harm (i.e., violence). Understanding that real-world violence is often spurred by a web of triggers, often combining several online tactics and pre-existing intersectional fissures in the social milieu, to result in targeted physical violence, we do not focus on any single divisive aspect (i.e., caste, gender, religion, or other identities of the victim and perpetrators) nor do we focus on just hate speech or mis/dis-information. Rather, our understanding of the intersectional causes of such triggers focuses our attempt at measuring the harm potential of online content, irrespective of whether it is hateful or not. In this paper, we discuss the development of a framework/annotation schema that allows annotating the data with different aspects of the text including its socio-political grounding and intent of the speaker (as expressed through mood and modality) that together contribute to it being a trigger for offline harm. We also give a comparative analysis and mapping of our framework with some of the existing frameworks.
Deep Learning-based Sentiment Analysis in Persian Language
Authors: Mohammad Heydari, Mohsen Khazeni, Mohammad Ali Soltanshahi
Date: 2024-03-17 00:00:00
Tasks: Deep Learning, Sentiment Analysis

Recently, there has been a growing interest in the use of deep learning techniques for tasks in natural language processing (NLP), with sentiment analysis being one of the most challenging areas, particularly in the Persian language. The vast amounts of content generated by Persian users on thousands of websites, blogs, and social networks such as Telegram, Instagram, and Twitter present a rich resource of information. Deep learning techniques have become increasingly favored for extracting insights from this extensive pool of raw data, although they face several challenges. In this study, we introduced and implemented a hybrid deep learning-based model for sentiment analysis, using customer review data from the Digikala Online Retailer website. We employed a variety of deep learning networks and regularization techniques as classifiers. Ultimately, our hybrid approach yielded an impressive performance, achieving an F1 score of 78.3 across three sentiment categories: positive, negative, and neutral.
Pre-Trained Language Models Represent Some Geographic Populations Better Than Others
Authors: Jonathan Dunn, Benjamin Adams, Harish Tayyar Madabushi
Date: 2024-03-16 00:00:00

This paper measures the skew in how well two families of LLMs represent diverse geographic populations. A spatial probing task is used with geo-referenced corpora to measure the degree to which pre-trained language models from the OPT and BLOOM series represent diverse populations around the world. Results show that these models perform much better for some populations than others. In particular, populations across the US and the UK are represented quite well while those in South and Southeast Asia are poorly represented. Analysis shows that both families of models largely share the same skew across populations. At the same time, this skew cannot be fully explained by sociolinguistic factors, economic factors, or geographic factors. The basic conclusion from this analysis is that pre-trained models do not equally represent the world's population: there is a strong skew towards specific geographic populations. This finding challenges the idea that a single model can be used for all populations.
Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases
Authors: Hang Yin, Liyao Xiang, Dong Ding, Yuheng He, Yihan Wu, Xinbing Wang, Chenghu Zhou
Date: 2024-03-16 00:00:00
Tasks: Contrastive Learning, Entity Alignment, Representation Learning

We investigate the entity alignment (EA) problem with unlabeled dangling cases, meaning that partial entities have no counterparts in the other knowledge graph (KG), and this type of entity remains unlabeled. To address this challenge, we propose the framework \textit{Lambda} for dangling detection and then entity alignment. Lambda features a GNN-based encoder called KEESA with spectral contrastive learning for EA and a positive-unlabeled learning algorithm for dangling detection called iPULE. iPULE offers theoretical guarantees of unbiasedness, uniform deviation bounds, and convergence. Experimental results demonstrate that each component contributes to overall performances that are superior to baselines, even when baselines additionally exploit 30\% of dangling entities labeled for training.
Pointer-Generator Networks for Low-Resource Machine Translation: Don't Copy That!
Authors: Niyati Bafna, Philipp Koehn, David Yarowsky
Date: 2024-03-16 00:00:00
Tasks: Machine Translation, NMT

While Transformer-based neural machine translation (NMT) is very effective in high-resource settings, many languages lack the necessary large parallel corpora to benefit from it. In the context of low-resource (LR) MT between two closely-related languages, a natural intuition is to seek benefits from structural "shortcuts", such as copying subwords from the source to the target, given that such language pairs often share a considerable number of identical words, cognates, and borrowings. We test Pointer-Generator Networks for this purpose for six language pairs over a variety of resource ranges, and find weak improvements for most settings. However, analysis shows that the model does not show greater improvements for closely-related vs. more distant language pairs, or for lower resource ranges, and that the models do not exhibit the expected usage of the mechanism for shared subwords. Our discussion of the reasons for this behaviour highlights several general challenges for LR NMT, such as modern tokenization strategies, noisy real-world conditions, and linguistic complexities. We call for better scrutiny of linguistically motivated improvements to NMT given the blackbox nature of Transformer models, as well as for a focus on the above problems in the field.
MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
Authors: Hanlei Zhang, Xin Wang, Hua Xu, Qianrui Zhou, Kai Gao, Jianhua Su, jinyue Zhao, Wenrui Li, Yanting Chen
Date: 2024-03-16 00:00:00
Tasks: Intent Recognition, Multimodal Intent Recognition

Multimodal intent recognition poses significant challenges, requiring the incorporation of non-verbal modalities from real-world contexts to enhance the comprehension of human intentions. Existing benchmark datasets are limited in scale and suffer from difficulties in handling out-of-scope samples that arise in multi-turn conversational interactions. We introduce MIntRec2.0, a large-scale benchmark dataset for multimodal intent recognition in multi-party conversations. It contains 1,245 dialogues with 15,040 samples, each annotated within a new intent taxonomy of 30 fine-grained classes. Besides 9,304 in-scope samples, it also includes 5,736 out-of-scope samples appearing in multi-turn contexts, which naturally occur in real-world scenarios. Furthermore, we provide comprehensive information on the speakers in each utterance, enriching its utility for multi-party conversational research. We establish a general framework supporting the organization of single-turn and multi-turn dialogue data, modality feature extraction, multimodal fusion, as well as in-scope classification and out-of-scope detection. Evaluation benchmarks are built using classic multimodal fusion methods, ChatGPT, and human evaluators. While existing methods incorporating nonverbal information yield improvements, effectively leveraging context information and detecting out-of-scope samples remains a substantial challenge. Notably, large language models exhibit a significant performance gap compared to humans, highlighting the limitations of machine learning methods in the cognitive intent understanding task. We believe that MIntRec2.0 will serve as a valuable resource, providing a pioneering foundation for research in human-machine conversational interactions, and significantly facilitating related applications. The full dataset and codes are available at https://github.com/thuiar/MIntRec2.0.
BEnQA: A Question Answering and Reasoning Benchmark for Bengali and English
Authors: Sheikh Shafayat, H M Quamran Hasan, Minhajur Rahman Chowdhury Mahim, Rifki Afina Putri, James Thorne, Alice Oh
Date: 2024-03-16 00:00:00
Tasks: Question Answering

In this study, we introduce BEnQA, a dataset comprising parallel Bengali and English exam questions for middle and high school levels in Bangladesh. Our dataset consists of approximately 5K questions covering several subjects in science with different types of questions, including factual, application, and reasoning-based questions. We benchmark several Large Language Models (LLMs) with our parallel dataset and observe a notable performance disparity between the models in Bengali and English. We also investigate some prompting methods, and find that Chain-of-Thought prompting is beneficial mostly on reasoning questions, but not so much on factual ones. We also find that appending English translation helps to answer questions in Bengali. Our findings point to promising future research directions for improving the performance of LLMs in Bengali and more generally in low-resource languages.
Towards Robustness and Diversity: Continual Learning in Dialog Generation with Text-Mixup and Batch Nuclear-Norm Maximization
Authors: Zihan Wang, Jiayu Xiao, Mengxiang Li, Zhongjiang He, Yongxiang Li, Chao Wang, Shuangyong Song
Date: 2024-03-16 00:00:00
Tasks: Continual Learning, Data Augmentation, Diversity, Language Modeling, Language Modelling

In our dynamic world where data arrives in a continuous stream, continual learning enables us to incrementally add new tasks/domains without the need to retrain from scratch. A major challenge in continual learning of language model is catastrophic forgetting, the tendency of models to forget knowledge from previously trained tasks/domains when training on new ones. This paper studies dialog generation under the continual learning setting. We propose a novel method that 1) uses \textit{Text-Mixup} as data augmentation to avoid model overfitting on replay memory and 2) leverages Batch-Nuclear Norm Maximization (BNNM) to alleviate the problem of mode collapse. Experiments on a $37$-domain task-oriented dialog dataset and DailyDialog (a $10$-domain chitchat dataset) demonstrate that our proposed approach outperforms the state-of-the-art in continual learning.
Zero-shot Generative Linguistic Steganography
Authors: Ke Lin, Yiyang Luo, Zijian Zhang, Ping Luo
Date: 2024-03-16 00:00:00
Tasks: In-Context Learning, Linguistic steganography

Generative linguistic steganography attempts to hide secret messages into covertext. Previous studies have generally focused on the statistical differences between the covertext and stegotext, however, ill-formed stegotext can readily be identified by humans. In this paper, we propose a novel zero-shot approach based on in-context learning for linguistic steganography to achieve better perceptual and statistical imperceptibility. We also design several new metrics and reproducible language evaluations to measure the imperceptibility of the stegotext. Our experimental results indicate that our method produces $1.926\times$ more innocent and intelligible stegotext than any other method.
RetinaQA: A Robust Knowledge Base Question Answering Model for both Answerable and Unanswerable Questions
Authors: Prayushi Faldu, Indrajit Bhattacharya, Mausam
Date: 2024-03-16 00:00:00
Tasks: Knowledge Base Question Answering, Question Answering, Retrieval, valid, World Knowledge

An essential requirement for a real-world Knowledge Base Question Answering (KBQA) system is the ability to detect the answerability of questions when generating logical forms. However, state-of-the-art KBQA models assume all questions to be answerable. Recent research has found that such models, when superficially adapted to detect answerability, struggle to satisfactorily identify the different categories of unanswerable questions, and simultaneously preserve good performance for answerable questions. Towards addressing this issue, we propose RetinaQA, a new KBQA model that unifies two key ideas in a single KBQA architecture: (a) discrimination over candidate logical forms, rather than generating these, for handling schema-related unanswerability, and (b) sketch-filling-based construction of candidate logical forms for handling data-related unaswerability. Our results show that RetinaQA significantly outperforms adaptations of state-of-the-art KBQA models in handling both answerable and unanswerable questions and demonstrates robustness across all categories of unanswerability. Notably, RetinaQA also sets a new state-of-the-art for answerable KBQA, surpassing existing models.
Two-step Automated Cybercrime Coded Word Detection using Multi-level Representation Learning
Authors: Yongyeon Kim, Byung-Won On, Ingyu Lee
Date: 2024-03-16 00:00:00
Tasks: Representation Learning

In social network service platforms, crime suspects are likely to use cybercrime coded words for communication by adding criminal meanings to existing words or replacing them with similar words. For instance, the word 'ice' is often used to mean methamphetamine in drug crimes. To analyze the nature of cybercrime and the behavior of criminals, quickly detecting such words and further understanding their meaning are critical. In the automated cybercrime coded word detection problem, it is difficult to collect a sufficient amount of training data for supervised learning and to directly apply language models that utilize context information to better understand natural language. To overcome these limitations, we propose a new two-step approach, in which a mean latent vector is constructed for each cybercrime through one of five different AutoEncoder models in the first step, and cybercrime coded words are detected based on multi-level latent representations in the second step. Moreover, to deeply understand cybercrime coded words detected through the two-step approach, we propose three novel methods: (1) Detection of new words recently coined, (2) Detection of words frequently appeared in both drug and sex crimes, and (3) Automatic generation of word taxonomy. According to our experimental results, among various AutoEncoder models, the stacked AutoEncoder model shows the best performance. Additionally, the F1-score of the two-step approach is 0.991, which is higher than 0.987 and 0.903 of the existing dark-GloVe and dark-BERT models. By analyzing the experimental results of the three proposed methods, we can gain a deeper understanding of drug and sex crimes.
Deciphering Hate: Identifying Hateful Memes and Their Targets
Authors: Eftekhar Hossain, Omar Sharif, Mohammed Moshiul Hoque, Sarah M. Preum
Date: 2024-03-16 00:00:00

Internet memes have become a powerful means for individuals to express emotions, thoughts, and perspectives on social media. While often considered as a source of humor and entertainment, memes can also disseminate hateful content targeting individuals or communities. Most existing research focuses on the negative aspects of memes in high-resource languages, overlooking the distinctive challenges associated with low-resource languages like Bengali (also known as Bangla). Furthermore, while previous work on Bengali memes has focused on detecting hateful memes, there has been no work on detecting their targeted entities. To bridge this gap and facilitate research in this arena, we introduce a novel multimodal dataset for Bengali, BHM (Bengali Hateful Memes). The dataset consists of 7,148 memes with Bengali as well as code-mixed captions, tailored for two tasks: (i) detecting hateful memes, and (ii) detecting the social entities they target (i.e., Individual, Organization, Community, and Society). To solve these tasks, we propose DORA (Dual cO attention fRAmework), a multimodal deep neural network that systematically extracts the significant modality features from the memes and jointly evaluates them with the modality-specific features to understand the context better. Our experiments show that DORA is generalizable on other low-resource hateful meme datasets and outperforms several state-of-the-art rivaling baselines.
Multi-party Response Generation with Relation Disentanglement
Authors: Tianhao Dai, Chengyu Huang, Lizi Liao
Date: 2024-03-16 00:00:00
Tasks: Disentanglement, Relation, Response Generation

Existing neural response generation models have achieved impressive improvements for two-party conversations, which assume that utterances are sequentially organized. However, many real-world dialogues involve multiple interlocutors and the structure of conversational context is much more complex, e.g. utterances from different interlocutors can occur "in parallel". Facing this challenge, there are works trying to model the relations among utterances or interlocutors to facilitate response generation with clearer context. Nonetheless, these methods rely heavily on such relations and all assume that these are given beforehand, which is impractical and hinders the generality of such methods. In this work, we propose to automatically infer the relations via relational thinking on subtle clues inside the conversation context without any human label, and leverage these relations to guide the neural response generation. Specifically, we first apply a deep graph random process to fully consider all possible relations among utterances in the conversational context. Then the inferred relation graphs are integrated with a variational auto-encoder framework to train a GAN for structure-aware response generation. Experimental results on the Ubuntu Internet Relay Chat (IRC) channel benchmark and the most recent Movie Dialogues show that our method outperforms various baseline models for multi-party response generation.
Can Large Language Models abstract Medical Coded Language?
Authors: Simon A. Lee, Timothy Lindsey
Date: 2024-03-16 00:00:00

Large Language Models (LLMs) have become a pivotal research area, potentially making beneficial contributions in fields like healthcare where they can streamline automated billing and decision support. However, the frequent use of specialized coded languages like ICD-10, which are regularly updated and deviate from natural language formats, presents potential challenges for LLMs in creating accurate and meaningful latent representations. This raises concerns among healthcare professionals about potential inaccuracies or ``hallucinations" that could result in the direct impact of a patient. Therefore, this study evaluates whether large language models (LLMs) are aware of medical code ontologies and can accurately generate names from these codes. We assess the capabilities and limitations of both general and biomedical-specific generative models, such as GPT, LLaMA-2, and Meditron, focusing on their proficiency with domain-specific terminologies. While the results indicate that LLMs struggle with coded language, we offer insights on how to adapt these models to reason more effectively.