ymoslem/acl-abstracts huggingface dataset:
Enhancing Semantics in Multimodal Chain of Thought via Soft Negative Sampling (2024)

Chain of thought (CoT) has proven useful for problems requiring complex reasoning. Many of these problems are both textual and multimodal. Given the inputs in different modalities, a model generates a rationale and then uses it to answer a question. Because of the hallucination issue, the generated soft negative rationales with high textual quality but illogical semantics do not always help improve answer accuracy. This study proposes a rationale generation method using soft negative sampling (SNSE-CoT) to mitigate hallucinations in multimodal CoT. Five methods were applied to generate soft negative samples that shared highly similar text but had different semantics from the original. Bidirectional margin loss (BML) was applied to introduce them into the traditional contrastive learning framework that involves only positive and negative samples. Extensive experiments on the ScienceQA dataset demonstrated the effectiveness of the proposed method. Code and data are released at https://github.com/zgMin/SNSE-CoT.
Enhancing Taiwanese Hokkien Dual Translation by Exploring and Standardizing of Four Writing Systems (2024)

Machine translation focuses mainly on high-resource languages (HRLs), while low-resource languages (LRLs) like Taiwanese Hokkien are relatively under-explored. The study aims to address this gap by developing a dual translation model between Taiwanese Hokkien and both Traditional Mandarin Chinese and English. We employ a pre-trained LLaMA 2-7B model specialized in Traditional Mandarin Chinese to leverage the orthographic similarities between Taiwanese Hokkien Han and Traditional Mandarin Chinese. Our comprehensive experiments involve translation tasks across various writing systems of Taiwanese Hokkien as well as between Taiwanese Hokkien and other HRLs. We find that the use of a limited monolingual corpus still further improves the model`s Taiwanese Hokkien capabilities. We then utilize our translation model to standardize all Taiwanese Hokkien writing systems into Hokkien Han, resulting in further performance improvements. Additionally, we introduce an evaluation method incorporating back-translation and GPT-4 to ensure reliable translation quality assessment even for LRLs. The study contributes to narrowing the resource gap for Taiwanese Hokkien and empirically investigates the advantages and limitations of pre-training and fine-tuning based on LLaMA 2.
Enhancing Text-to-SQL Capabilities of Large Language Models through Tailored Promptings (2024)

Large language models (LLMs) with prompting have achieved encouraging results on many natural language processing (NLP) tasks based on task-tailored promptings. Text-to-SQL is a critical task that generates SQL queries from natural language questions. However, prompting on LLMs haven`t show superior performance on Text-to-SQL task due to the absence of tailored promptings. In this work, we propose three promptings specifically designed for Text-to-SQL: SL-prompt, CC-prompt, and SL+CC prompt. SL-prompt is designed to guide LLMs to identify relevant tables; CC-prompt directs LLMs to generate SQL clause by clause; and SL+CC prompt is proposed to combine the strengths of these above promptings. The three prompting strategies makes three solutions for Text-to-SQL. Then, another prompting strategy, the RS-prompt is proposed to direct LLMs to select the best answer from the results of the solutions. We conducted extensive experiments, and experimental results show that our method achieved an execution accuracy of 86.2{\%} and a test-suite accuracy of 76.9{\%}, which is 1.1{\%}, and 2.7{\%} higher than the current state-of-the-art Text-to-SQL methods, respectively. The results confirmed that the proposed promptings enhanced the capabilities of LLMs on Text-to-SQL. Experimental results also show that the granularity of schema linking and the order of clause generation have great impact on the performance, which are considered little in previous research.
Enhancing Translation Ability of Large Language Models by Leveraging Task-Related Layers (2024)

Fine-tuning Large Language Models (LLMs) for machine translation is effective but costly. It also increases the risk of overfitting and catastrophic forgetting, especially when training data is limited. To tackle these challenges, we propose a novel method that involves adjusting task-related layers in large models to better harness their machine translation capabilities. This method aims to retain the model`s knowledge on other tasks while optimizing performance on translation tasks. By revealing the structure and characteristics of attention weights through singular value decomposition (SVD), we can make fine adjustments to specific layers, leveraging the model`s potential for more accurate and efficient translations. Our method not only addresses computational resource consumption and catastrophic forgetting but also offers a new perspective on utilizing the capabilities of large models effectively. Experimental validation shows that adjusting task-related layers significantly improves performance on translation tasks while maintaining stability and accuracy on other tasks. This finding provides valuable insights for fine-tuning and applying large models, advancing the field of machine translation.
Enhancing Unrestricted Cross-Document Event Coreference with Graph Reconstruction Networks (2024)

Event Coreference Resolution remains a challenging discourse-oriented task within the domain of Natural Language Processing. In this paper we propose a methodology where we combine traditional mention-pair coreference models with a lightweight and modular graph reconstruction algorithm. We show that building graph models on top of existing mention-pair models leads to improved performance for both a wide range of baseline mention-pair algorithms as well as a recently developed state-of-the-art model and this at virtually no added computational cost. Moreover, additional experiments seem to indicate that our method is highly robust in low-data settings and that its performance scales with increases in performance for the underlying mention-pair models.
Enhancing Writing Proficiency Classification in Developmental Education: The Quest for Accuracy (2024)

Developmental Education (DevEd) courses align students' college-readiness skills with higher education literacy demands. These courses often use automated assessment tools like Accuplacer for student placement. Existing literature raises concerns about these exams' accuracy and placement precision due to their narrow representation of the writing process. These concerns warrant further attention within the domain of automatic placement systems, particularly in the establishment of a reference corpus of annotated essays for these systems' machine/deep learning. This study aims at an enhanced annotation procedure to assess college students' writing patterns more accurately. It examines the efficacy of machine-learning-based DevEd placement, contrasting Accuplacer`s classification of 100 college-intending students' essays into two levels (Level 1 and 2) against that of 6 human raters. The classification task encompassed the assessment of the 6 textual criteria currently used by Accuplacer: mechanical conventions, sentence variety {\&} style, idea development {\&} support, organization {\&} structure, purpose {\&} focus, and critical thinking. Results revealed low inter-rater agreement, both on the individual criteria and the overall classification, suggesting human assessment of writing proficiency can be inconsistent in this context. To achieve a more accurate determination of writing proficiency and improve DevEd placement, more robust classification methods are thus required.
Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic (2024)

Recent advancements in large language models have showcased their remarkable generalizability across various domains. However, their reasoning abilities still have significant room for improvement, especially when confronted with scenarios requiring multi-step reasoning. Although large language models possess extensive knowledge, their reasoning often fails to effectively utilize this knowledge to establish a coherent thinking paradigm. These models sometimes show hallucinations as their reasoning procedures are unconstrained by logical principles. Aiming at improving the zero-shot chain-of-thought reasoning ability of large language models, we propose LoT (Logical Thoughts), a self-improvement prompting framework that leverages principles rooted in symbolic logic, particularly Reductio ad Absurdum, to systematically verify and rectify the reasoning processes step by step. Experimental evaluations conducted on language tasks in diverse domains, including arithmetic, commonsense, symbolic, causal inference, and social problems, demonstrate the efficacy of enhanced reasoning by logic. The implementation code for LoT can be accessed at: https://github.com/xf-zhao/LoT.
Enough Is Enough! a Case Study on the Effect of Data Size for Evaluation Using Universal Dependencies (2024)

When creating a new dataset for evaluation, one of the first considerations is the size of the dataset. If our evaluation data is too small, we risk making unsupported claims based on the results on such data. If, on the other hand, the data is too large, we waste valuable annotation time and costs that could have been used to widen the scope of our evaluation (i.e. annotate for more domains/languages). Hence, we investigate the effect of the size and a variety of sampling strategies of evaluation data to optimize annotation efforts, using dependency parsing as a test case. We show that for in-language in-domain datasets, 5,000 tokens is enough to obtain a reliable ranking of different parsers; especially if the data is distant enough from the training split (otherwise, we recommend 10,000). In cross-domain setups, the same amounts are required, but in cross-lingual setups much less (2,000 tokens) is enough.
Enriching a Time-Domain Astrophysics Corpus with Named Entity, Coreference and Astrophysical Relationship Annotations (2024)

Interest in Astrophysical Natural Language Processing (NLP) has increased recently, fueled by the development of specialized language models for information extraction. However, the scarcity of annotated resources for this domain is still a significant challenge. Most existing corpora are limited to Named Entity Recognition (NER) tasks, leaving a gap in resource diversity. To address this gap and facilitate a broader spectrum of NLP research in astrophysics, we introduce astroECR, an extension of our previously built Time-Domain Astrophysics Corpus (TDAC). Our contributions involve expanding it to cover named entities, coreferences, annotations related to astrophysical relationships, and normalizing celestial object names. We showcase practical utility through baseline models for four NLP tasks and provide the research community access to our corpus, code, and models.
Enriching Word Usage Graphs with Cluster Definitions (2024)

We present a dataset of word usage graphs (WUGs), where the existing WUGs for multiple languages are enriched with cluster labels functioning as sense definitions. They are generated from scratch by fine-tuned encoder-decoder language models. The conducted human evaluation has shown that these definitions match the existing clusters in WUGs better than the definitions chosen from WordNet by two baseline systems. At the same time, the method is straightforward to use and easy to extend to new languages. The resulting enriched datasets can be extremely helpful for moving on to explainable semantic change modeling.
Ensembles of Hybrid and End-to-End Speech Recognition. (2024)

We propose a method to combine the hybrid Kaldi-based Automatic Speech Recognition (ASR) system with the end-to-end wav2vec 2.0 XLS-R ASR using confidence measures. Our research is focused on the low-resource Irish language. Given the limited available open-source resources, neither the standalone hybrid ASR nor the end-to-end ASR system can achieve optimal performance. By applying the Recognizer Output Voting Error Reduction (ROVER) technique, we illustrate how ensemble learning could facilitate mutual error correction between both ASR systems. This paper outlines the strategies for merging the hybrid Kaldi ASR model and the end-to-end XLS-R model with the help of confidence scores. Although contemporary state-of-the-art end-to-end ASR models face challenges related to prediction overconfidence, we utilize Renyi`s entropy-based confidence approach, tuned with temperature scaling, to align it with the Kaldi ASR confidence. Although there was no significant difference in the Word Error Rate (WER) between the hybrid and end-to-end ASR, we could achieve a notable reduction in WER after ensembling through ROVER. This resulted in an almost 14{\%} Word Error Rate Reduction (WERR) on our primary test set and an approximately 20{\%} WERR on other noisy and imbalanced test data.
EpiGEN: An Efficient Multi-Api Code GENeration Framework under Enterprise Scenario (2024)

In recent years, Large Language Models (LLMs) have demonstrated exceptional performance in code-generation tasks. However, under enterprise scenarios where private APIs are pre-built, general LLMs often fail to meet expectations. Existing approaches are confronted with drawbacks of high resource consumption and inadequate handling of multi-API tasks. To address these challenges, we propose EpiGEN, an Efficient multi-Api code GENeration framework under enterprise scenario. It consists of three core modules: Task Decomposition Module (TDM), API Retrieval Module (ARM), and Code Generation Module (CGM), in which Langchain played an important role. Through a series of experiments, EpiGEN shows good acceptability and readability, compared to fully fine-tuned LLM with a larger number of parameters. Particularly, in medium and hard level tasks, the performance of EpiGEN on a single-GPU machine even surpasses that of a fully fine-tuned LLM that requires multi-GPU configuration. Generally, EpiGEN is model-size agnostic, facilitating a balance between the performance of code generation and computational requirements.
EpLSA: Synergy of Expert-prefix Mixtures and Task-Oriented Latent Space Adaptation for Diverse Generative Reasoning (2024)

Existing models for diverse generative reasoning still struggle to generate multiple unique and plausible results. Through an in-depth examination, we argue that it is critical to leverage a mixture of experts as prefixes to enhance the diversity of generated results and make task-oriented adaptation in the latent space of the generation models to improve the quality of the responses. At this point, we propose EpLSA, an innovative model based on the synergy of expert-prefix mixtures and task-oriented latent space adaptation for diverse generative reasoning. Specifically, we use expert-prefixes mixtures to encourage the model to create multiple responses with different semantics and design a loss function to address the problem that the semantics is interfered by the expert-prefixes. Meanwhile, we design a task-oriented adaptation block to make the pre-trained encoder within the generation model more effectively adapted to the pre-trained decoder in the latent space, thus further improving the quality of the generated text. Extensive experiments on three different types of generative reasoning tasks demonstrate that EpLSA outperforms existing baseline models in terms of both the quality and diversity of the generated outputs. Our code is publicly available at https://github.com/IMU-MachineLearningSXD/EpLSA.
EPOQUE: An English-Persian Quality Estimation Dataset (2024)

Translation quality estimation (QE) is an important component in real-world machine translation applications. Unfortunately, human labeled QE datasets, which play an important role in developing and assessing QE models, are only available for limited language pairs. In this paper, we present the first English-Persian QE dataset, called EPOQUE, which has manually annotated direct assessment labels. EPOQUE contains 1000 sentences translated from English to Persian and annotated by three human annotators. It is publicly available, and thus can be used as a zero-shot test set, or for other scenarios in future work. We also evaluate and report the performance of two state-of-the-art QE models, i.e., Transquest and CometKiwi, as baselines on our dataset. Furthermore, our experiments show that using a small subset of the proposed dataset containing 300 sentences to fine-tune Transquest, can improve its performance by more that 8{\%} in terms of the Pearson correlation with a held-out test set.
EROS:Entity-Driven Controlled Policy Document Summarization (2024)

Privacy policy documents have a crucial role in educating individuals about the collection, usage, and protection of users' personal data by organizations. However, they are notorious for their lengthy, complex, and convoluted language especially involving privacy-related entities. Hence, they pose a significant challenge to users who attempt to comprehend organization`s data usage policy. In this paper, we propose to enhance the interpretability and readability of policy documents by using controlled abstractive summarization {--} we enforce the generated summaries to include critical privacy-related entities (e.g., data and medium) and organization`s rationale (e.g., target and reason) in collecting those entities. To achieve this, we develop PD-Sum, a policy-document summarization dataset with marked privacy-related entity labels. Our proposed model, EROS, identifies critical entities through a span-based entity extraction model and employs them to control the information content of the summaries using proximal policy optimization (PPO). Comparison shows encouraging improvement over various baselines. Furthermore, we furnish qualitative and human evaluations to establish the efficacy of EROS.
Error Analysis of NLP Models and Non-Native Speakers of English Identifying Sarcasm in Reddit Comments (2024)

This paper summarises the differences and similarities found between humans and three natural language processing models when attempting to identify whether English online comments are sarcastic or not. Three models were used to analyse 300 comments from the FigLang 2020 Reddit Dataset, with and without context. The same 300 comments were also given to 39 non-native speakers of English and the results were compared. The aim was to find whether there were any results that could be applied to English as a Foreign Language (EFL) teaching. The results showed that there were similarities between the models and non-native speakers, in particular the logistic regression model. They also highlighted weaknesses with both non-native speakers and the models in detecting sarcasm when the comments included political topics or were phrased as questions. This has potential implications for how the EFL teaching industry could implement the results of error analysis of NLP models in teaching practices.
Error-Robust Retrieval for Chinese Spelling Check (2024)

Chinese Spelling Check (CSC) aims to detect and correct error tokens in Chinese contexts, which has a wide range of applications. However, it is confronted with the challenges of insufficient annotated data and the issue that previous methods may actually not fully leverage the existing datasets. In this paper, we introduce our plug-and-play retrieval method with error-robust information for Chinese Spelling Check (RERIC), which can be directly applied to existing CSC models. The datastore for retrieval is built completely based on the training data, with elaborate designs according to the characteristics of CSC. Specifically, we employ multimodal representations that fuse phonetic, morphologic, and contextual information in the calculation of query and key during retrieval to enhance robustness against potential errors. Furthermore, in order to better judge the retrieved candidates, the n-gram surrounding the token to be checked is regarded as the value and utilized for specific reranking. The experiment results on the SIGHAN benchmarks demonstrate that our proposed method achieves substantial improvements over existing work.
EsCoLA: Spanish Corpus of Linguistic Acceptability (2024)

Acceptability is one of the General Language Understanding Evaluation Benchmark (GLUE) probing tasks proposed to assess the linguistic capabilities acquired by a deep-learning transformer-based language model (LM). In this paper, we introduce the Spanish Corpus of Linguistic Acceptability EsCoLA. EsCoLA has been developed following the example of other linguistic acceptability data sets for English, Italian, Norwegian or Russian, with the aim of having a complete GLUE benchmark for Spanish. EsCoLA consists of 11,174 sentences and their acceptability judgements as found in well-known Spanish reference grammars. Additionally, all sentences have been annotated with the class of linguistic phenomenon the sentence is an example of, also following previous practices. We also provide as task baselines the results of fine-tuning four different language models with this data set and the results of a human annotation experiment. Results are also analyzed and commented to guide future research. EsCoLA is released under a CC-BY 4.0 license and freely available at https://doi.org/10.34810/data1138.
ESCP: Enhancing Emotion Recognition in Conversation with Speech and Contextual Prefixes (2024)

Emotion Recognition in Conversation (ERC) aims to analyze the speaker`s emotional state in a conversation. Fully mining the information in multimodal and historical utterances plays a crucial role in the performance of the model. However, recent works in ERC focus on historical utterances modeling and generally concatenate the multimodal features directly, which neglects mining deep multimodal information and brings redundancy at the same time. To address the shortcomings of existing models, we propose a novel model, termed Enhancing Emotion Recognition in Conversation with Speech and Contextual Prefixes (ESCP). ESCP employs a directed acyclic graph (DAG) to model historical utterances in a conversation and incorporates a contextual prefix containing the sentiment and semantics of historical utterances. By adding speech and contextual prefixes, the inter- and intra-modal emotion information is efficiently modeled using the prior knowledge of the large-scale pre-trained model. Experiments conducted on several public benchmarks demonstrate that the proposed approach achieves state-of-the-art (SOTA) performances. These results affirm the effectiveness of the novel ESCP model and underscore the significance of incorporating speech and contextual prefixes to guide the pre-trained model.
ESDM: Early Sensing Depression Model in Social Media Streams (2024)

Depression impacts millions worldwide, with increasing efforts to use social media data for early detection and intervention. Traditional Risk Detection (TRD) uses a user`s complete posting history for predictions, while Early Risk Detection (ERD) seeks early detection in a user`s posting history, emphasizing the importance of prediction earliness. However, ERD remains relatively underexplored due to challenges in balancing accuracy and earliness, especially with evolving partial data. To address this, we introduce the Early Sensing Depression Model (ESDM), which comprises two modules classification with partial information module (CPI) and decision for classification moment module (DMC), alongside an early detection loss function. Experiments show ESDM outperforms benchmarks in both earliness and accuracy.