ymoslem/acl-abstracts huggingface dataset:
Mixture-of-LoRAs: An Efficient Multitask Tuning Method for Large Language Models (2024)

Instruction Tuning has the potential to stimulate or enhance specific capabilities of large language models (LLMs). However, achieving the right balance of data is crucial to prevent catastrophic forgetting and interference between tasks. To address these limitations and enhance training flexibility, we propose the Mixture-of-LoRAs (MoA) architecture which is a novel and parameter-efficient tuning method designed for multi-task learning with LLMs. In this paper, we start by individually training multiple domain-specific LoRA modules using corresponding supervised corpus data. These LoRA modules can be aligned with the expert design principles observed in Mixture-of-Experts (MoE). Subsequently, we combine the multiple LoRAs using an explicit routing strategy and introduce domain labels to facilitate multi-task learning, which help prevent interference between tasks and ultimately enhances the performance of each individual task. Furthermore, each LoRA model can be iteratively adapted to a new domain, allowing for quick domain-specific adaptation. Experiments on diverse tasks demonstrate superior and robust performance, which can further promote the wide application of domain-specific LLMs.
Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding (2024)

Deep multimodal semantic understanding that goes beyond the mere superficial content relation mining has received increasing attention in the realm of artificial intelligence. The challenges of collecting and annotating high-quality multi-modal data have underscored the significance of few-shot learning. In this paper, we focus on two critical tasks under this context: few-shot multi-modal sarcasm detection (MSD) and multi-modal sentiment analysis (MSA). To address them, we propose Mixture-of-Prompt-Experts with Block-Aware Prompt Fusion (MoPE-BAF), a novel multi-modal soft prompt framework based on the unified vision-language model (VLM). Specifically, we design three experts of soft prompts: a text prompt and an image prompt that extract modality-specific features to enrich the single-modal representation, and a unified prompt to assist multi-modal interaction. Additionally, we reorganize Transformer layers into several blocks and introduce cross-modal prompt attention between adjacent blocks, which smoothens the transition from single-modal representation to multi-modal fusion. On both MSD and MSA datasets in few-shot setting, our proposed model not only surpasses the 8.2B model InstructBLIP with merely 2{\%} parameters (150M), but also significantly outperforms other widely-used prompt methods on VLMs or task-specific methods.
MKeCL: Medical Knowledge-Enhanced Contrastive Learning for Few-shot Disease Diagnosis (2024)

Artificial intelligence (AI)-aided disease prediction has gained extensive research interest due to its capability to support clinical decision-making. Existing works mainly formulate disease prediction as a multi-label classification problem and use historical Electronic Medical Records (EMR) to train supervised models. However, in real-world clinics, such purely data-driven approaches pose two main challenges: 1) long tail problem: there are excessive EMRs for common diseases and insufficient EMRs for rare diseases, thus training over an imbalanced data set could result in a biased model that ignores rare diseases in diagnosis; 2) easily misdiagnosed diseases: some diseases can be easily distinguished while others sharing analogous conditions are much more difficult. General classification models without emphasizing easily misdiagnosed diseases may generate incorrect predictions. To tackle these two problems, we propose a Medical Knowledge-Enhanced Contrastive Learning (\textbf{MKeCL}) approach to disease diagnosis in this paper. MKeCL incorporates medical knowledge graphs and medical licensing exams in modeling in order to compensate for the insufficient information on rare diseases; To handle hard-to-diagnose diseases, MKeCL introduces a contrastive learning strategy to separate diseases that are easily misdiagnosed. Moreover, we establish a new benchmark, named \textbf{Jarvis-D}, which contains clinical EMRs collected from various hospitals. Experiments on real clinical EMRs show that the proposed MKeCL outperforms existing disease prediction approaches, especially in the setting of few-shot and zero-shot scenarios.
MLDSP-MA: Multidimensional Attention for Multi-Round Long Dialogue Sentiment Prediction (2024)

The intelligent chatbot takes dialogue sentiment prediction as the core, and it has to tackle long dialogue sentiment prediction problems in many real-world applications. Current state-of-the-art methods usually employ attention-based dialogue sentiment prediction models. However, as the conversation progresses, more topics are involved and the changes in sentiments become more frequent, which leads to a sharp decline in the accuracy and efficiency of the current methods. Therefore, we propose a Multi-round Long Dialogue Sentiment Prediction based on Multidimensional Attention (MLDSP-MA), which can focus on different topics. In particular, MLSDP-MA leverages a sliding window to capture different topics and traverses all historical dialogues. In each sliding window, the contextual dependency, sentiment persistence, and sentiment infectivity are characterized, and local attention cross fusion is performed. To learn dialogue sentiment globally, global attention is proposed to iteratively learn comprehensive sentiments from historical dialogues, and finally integrate with local attention. We conducted extensive experimental research on publicly available dialogue datasets. The experimental results show that, compared to the current state-of-the-art methods, our model improves by 3.5{\%} in accuracy and 5.7{\%} in Micro-F1 score.
MMAD:Multi-modal Movie Audio Description (2024)

Audio Description (AD) aims to generate narrations of information that is not accessible through unimodal hearing in movies to aid the visually impaired in following film narratives. Current solutions rely heavily on manual work, resulting in high costs and limited scalability. While automatic methods have been introduced, they often yield descriptions that are sparse and omit key details. ddressing these challenges, we propose a novel automated pipeline, the Multi-modal Movie Audio Description (MMAD). MMAD harnesses the capabilities of three key modules as well as the power of Llama2 to augment the depth and breadth of the generated descriptions. Specifically, first, we propose an Audio-aware Feature Enhancing Module to provide the model with multi-modal perception capabilities, enriching the background descriptions with a more comprehensive understanding of the environmental features. Second, we propose an Actor-tracking-aware Story Linking Module to aid in the generation of contextual and character-centric descriptions, thereby enhancing the richness of character depictions. Third, we incorporate a Subtitled Movie Clip Contextual Alignment Module, supplying semantic information about various time periods throughout the movie, which facilitates the consideration of the full movie narrative context when describing silent segments, thereby enhancing the richness of the descriptions. Experiments on widely used datasets convincingly demonstrates that MMAD significantly surpasses existing strong baselines in performance, establishing a new state-of-the-art in the field. Our code will be released at https://github.com/Daria8976/MMAD.
MMAPS: End-to-End Multi-Grained Multi-Modal Attribute-Aware Product Summarization (2024)

Given the long textual product information and the product image, Multi-modal Product Summarization (MPS) aims to increase customers' desire to purchase by highlighting product characteristics with a short textual summary. Existing MPS methods can produce promising results. Nevertheless, they still 1) lack end-to-end product summarization, 2) lack multi-grained multi-modal modeling, and 3) lack multi-modal attribute modeling. To improve MPS, we propose an end-to-end multi-grained multi-modal attribute-aware product summarization method (MMAPS) for generating high-quality product summaries in e-commerce. MMAPS jointly models product attributes and generates product summaries. We design several multi-grained multi-modal tasks to better guide the multi-modal learning of MMAPS. Furthermore, we model product attributes based on both text and image modalities so that multi-modal product characteristics can be manifested in the generated summaries. Extensive experiments on a real large-scale Chinese e-commence dataset demonstrate that our model outperforms state-of-the-art product summarization methods w.r.t. several summarization metrics. Our code is publicly available at: https://github.com/KDEGroup/MMAPS.
MM-IGLU: Multi-Modal Interactive Grounded Language Understanding (2024)

This paper explores Interactive Grounded Language Understanding (IGLU) challenges within Human-Robot Interaction (HRI). In this setting, a robot interprets user commands related to its environment, aiming to discern whether a specific command can be executed. If faced with ambiguities or incomplete data, the robot poses relevant clarification questions. Drawing from the NeurIPS 2022 IGLU competition, we enrich the dataset by introducing our multi-modal data and natural language descriptions in \textit{MM-IGLU: Multi-Modal Interactive Grounded Language Understanding}. Utilizing a BART-based model that integrates the user`s statement with the environment`s description, and a cutting-edge Multi-Modal Large Language Model that merges both visual and textual data, we offer a valuable resource for ongoing research in the domain. Additionally, we discuss the evaluation methods for such tasks, highlighting potential limitations imposed by traditional string-match-based evaluations on this intricate multi-modal challenge. Moreover, we provide an evaluation benchmark based on human judgment to address the limits and capabilities of such baseline models. This resource is released on a dedicated GitHub repository at https://github.com/crux82/MM-IGLU.
MNER-MI: A Multi-image Dataset for Multimodal Named Entity Recognition in Social Media (2024)

Recently, multimodal named entity recognition (MNER) has emerged as a vital research area within named entity recognition. However, current MNER datasets and methods are predominantly based on text and a single accompanying image, leaving a significant research gap in MNER scenarios involving multiple images. To address the critical research gap and enhance the scope of MNER for real-world applications, we propose a novel human-annotated MNER dataset with multiple images called MNER-MI. Additionally, we construct a dataset named MNER-MI-Plus, derived from MNER-MI, to ensure its generality and applicability. Based on these datasets, we establish a comprehensive set of strong and representative baselines and we further propose a simple temporal prompt model with multiple images to address the new challenges in multi-image scenarios. We have conducted extensive experiments to demonstrate that considering multiple images provides a significant improvement over a single image and can offer substantial benefits for MNER. Furthermore, our proposed method achieves state-of-the-art results on both MNER-MI and MNER-MI-Plus, demonstrating its effectiveness. The datasets and source code can be found at https://github.com/JinFish/MNER-MI.
Modalities Should Be Appropriately Leveraged: Uncertainty Guidance for Multimodal Chinese Spelling Correction (2024)

Chinese spelling correction (CSC) aims to detect and correct spelling errors in Chinese texts. Most spelling errors are phonetically or graphically similar to the correct ones. Thus, recent works introduce multimodal features to obtain achievements. In this paper, we found that different spelling errors have various biases to each modality, highlighting the importance of appropriately exploiting multimodal features. To achieve this goal, we propose the UGMSC framework, which incorporates uncertainty into both the feature learning and correction stages. Specifically, the UGMSC framework makes predictions with multimodal features and estimates the uncertainty of the corresponding modalities. Then it dynamically fuses the features of all modalities for model learning, and performs spelling correction under the uncertainty-guided strategy. Experimental results on three public datasets demonstrate that the proposed approach provides a significant improvement compared with previous strong multimodal models. The proposed framework is model-agnostic and can be easily applied to other multimodal models.
MoDE-CoTD: Chain-of-Thought Distillation for Complex Reasoning Tasks with Mixture of Decoupled LoRA-Experts (2024)

Chain-of-thought Distillation (CoTD) aims at distilling Chain-of-thought (CoT) reasoning ability of large language models (LLMs) to much smaller student models. The core of CoTD is using a large teacher model to generate rationales and fine-tune smaller student models. However, current Chain-of-thought Distillation works have the following limitations: 1) Student models are separately distilled from specific reasoning tasks and lack a collaboration mechanism, hindering the enhancement of reasoning performance through collaboration among various reasoning tasks. 2) The parameter update of student models severely harms the CoT reasoning ability on other unseen reasoning tasks not included in the distillation process. In this work, we introduce a novel CoT Distillation method, MoDE-CoTD, which decouples the CoT reasoning abilities out of the student model by distilling multiple LoRA-Experts and freezing the parameters of the student model. Sequentially, LoRA-Experts are combined and adapted to handle both seen and unseen reasoning tasks, enabling collaboration among diverse reasoning tasks to further enhance CoT reasoning performance. Experimental results on 14 datasets (including 4 unseen datasets) demonstrate the strength of MoDE-CoTD, with an average accuracy gain of 6.3{\%} on seen datasets and 7.8{\%} on unseen datasets.
Model-Agnostic Cross-Lingual Training for Discourse Representation Structure Parsing (2024)

Discourse Representation Structure (DRS) is an innovative semantic representation designed to capture the meaning of texts with arbitrary lengths across languages. The semantic representation parsing is essential for achieving natural language understanding through logical forms. Nevertheless, the performance of DRS parsing models remains constrained when trained exclusively on monolingual data. To tackle this issue, we introduce a cross-lingual training strategy. The proposed method is model-agnostic yet highly effective. It leverages cross-lingual training data and fully exploits the alignments between languages encoded in pre-trained language models. The experiments conducted on the standard benchmarks demonstrate that models trained using the cross-lingual training method exhibit significant improvements in DRS clause and graph parsing in English, German, Italian and Dutch. Comparing our final models to previous works, we achieve state-of-the-art results in the standard benchmarks. Furthermore, the detailed analysis provides deep insights into the performance of the parsers, offering inspiration for future research in DRS parsing.
Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal Summarization and Text-Units-Text Generation (2024)

Health coaching helps patients achieve personalized and lifestyle-related goals, effectively managing chronic conditions and alleviating mental health issues. It is particularly beneficial, however cost-prohibitive, for low-socioeconomic status populations due to its highly personalized and labor-intensive nature. In this paper, we propose a neuro-symbolic goal summarizer to support health coaches in keeping track of the goals and a text-units-text dialogue generation model that converses with patients and helps them create and accomplish specific goals for physical activities. Our models outperform previous state-of-the-art while eliminating the need for predefined schema and corresponding annotation. We also propose a new health coaching dataset extending previous work and a metric to measure the unconventionality of the patient`s response based on data difficulty, facilitating potential coach alerts during deployment.
Modeling Orthographic Variation Improves NLP Performance for Nigerian Pidgin (2024)

Nigerian Pidgin is an English-derived contact language and is traditionally an oral language, spoken by approximately 100 million people. No orthographic standard has yet been adopted, and thus the few available Pidgin datasets that exist are characterised by noise in the form of orthographic variations. This contributes to under-performance of models in critical NLP tasks. The current work is the first to describe various types of orthographic variations commonly found in Nigerian Pidgin texts, and model this orthographic variation. The variations identified in the dataset form the basis of a phonetic-theoretic framework for word editing, which is used to generate orthographic variations to augment training data. We test the effect of this data augmentation on two critical NLP tasks: machine translation and sentiment analysis. The proposed variation generation framework augments the training data with new orthographic variants which are relevant for the test set but did not occur in the training set originally. Our results demonstrate the positive effect of augmenting the training data with a combination of real texts from other corpora as well as synthesized orthographic variation, resulting in performance improvements of 2.1 points in sentiment analysis and 1.4 BLEU points in translation to English.
Modeling the Quality of Dialogical Explanations (2024)

Explanations are pervasive in our lives. Mostly, they occur in dialogical form where an \textit{explainer} discusses a concept or phenomenon of interest with an \textit{explainee}. Leaving the explainee with a clear understanding is not straightforward due to the knowledge gap between the two participants. Previous research looked at the interaction of explanation moves, dialogue acts, and topics in successful dialogues with expert explainers. However, daily-life explanations often fail, raising the question of what makes a dialogue successful. In this work, we study explanation dialogues in terms of the interactions between the explainer and explainee and how they correlate with the quality of explanations in terms of a successful understanding on the explainee`s side. In particular, we first construct a corpus of 399 dialogues from the Reddit forum \textit{Explain Like I am Five} and annotate it for interaction flows and explanation quality. We then analyze the interaction flows, comparing them to those appearing in expert dialogues. Finally, we encode the interaction flows using two language models that can handle long inputs, and we provide empirical evidence for the effectiveness boost gained through the encoding in predicting the success of explanation dialogues.
Modelling and Linking an Old Latin-Portuguese Dictionary to the LiLa Knowledge Base (2024)

This paper describes the steps undertaken to include data from Antonio Velez`s bilingual Latin-Portuguese dictionary (Index Totius Artis, 1744) into the LiLa Knowledge Base of interoperable linguistic resources for Latin. The paper focuses on how the lexical and lexicographic information of the source dictionary was modelled by using respectively the Lexicon Model for Ontologies (OntoLex-lemon) and its lexicog module. The linking process of the dictionary entries with those of the LiLa collection of Latin lemmas is detailed, discussing issues in dealing with ambiguities and typographical errors found in the source. The result is the first Latin-Portuguese lexical resource made interoperable with the (meta)data of the other linguistic resources for Latin interlinked in the LiLa Knowledge Base, providing new ways of assessing the dictionary information or using its content as starting point to explore the connections with other interlinked linguistic resources. A couple of use case scenarios illustrate those possibilities.
Modelling Argumentation for an User Opinion Aggregation Tool (2024)

We introduce an argumentation annotation scheme that models basic argumentative structure and additional contextual details across diverse user opinion domains. Drawing from established argumentation modeling approaches and related theory on user opinions, the scheme integrates the concepts of argumentative components, specificity, sentiment and aspects of the user opinion domain. Our freely available dataset includes 1,016 user opinions with 7,266 sentences, spanning products from 19 e-commerce categories, restaurants, hotels, local services, and mobile applications. Utilizing the dataset, we trained three transformer-based models, demonstrating their efficacy in predicting the annotated classes for identifying argumentative statements and contextual details from user opinion documents. Finally, we evaluate a prototypical dashboard that integrates the model inferences to aggregate information and rank exemplary products based on a vast array of user opinions. Early results from an experimental evaluation with eighteen users include positive user perceptions but also highlight challenges when condensing detailed argumentative information to users.
MoNMT: Modularly Leveraging Monolingual and Bilingual Knowledge for Neural Machine Translation (2024)

The effective use of monolingual and bilingual knowledge represents a critical challenge within the neural machine translation (NMT) community. In this paper, we propose a modular strategy that facilitates the cooperation of these two types of knowledge in translation tasks, while avoiding the issue of catastrophic forgetting and exhibiting superior model generalization and robustness. Our model is comprised of three functionally independent modules: an encoding module, a decoding module, and a transferring module. The former two acquire large-scale monolingual knowledge via self-supervised learning, while the latter is trained on parallel data and responsible for transferring latent features between the encoding and decoding modules. Extensive experiments in multi-domain translation tasks indicate our model yields remarkable performance, with up to 7 BLEU improvements in out-of-domain tests over the conventional pretrain-and-finetune approach. Our codes are available at https://github.com/NLP2CT/MoNMT.
Monolingual Paraphrase Detection Corpus for Low Resource Pashto Language at Sentence Level (2024)

Paraphrase detection is a task to identify if two sentences are semantically similar or not. It plays an important role in maintaining the integrity of written work such as plagiarism detection and text reuse detection. Formerly, researchers focused on developing large corpora for English. However, no research has been conducted on sentence-level paraphrase detection in low-resource Pashto language. To bridge this gap, we introduce the first fully manually annotated Pashto sentential paraphrase detection corpus collected from authentic cases in journalism covering 10 different domains, including Sports, Health, Environment, and more. Our proposed corpus contains 6,727 sentences, encompassing 3,687 paraphrased and 3,040 non-paraphrased. Experimental findings reveal that our proposed corpus is sufficient to train XLM-RoBERTa to accurately detect paraphrased sentence pairs in Pashto with an F1 score of 84{\%}. To compare our corpus with those in other languages, we also applied our fine-tuned model to the Indonesian and English paraphrase datasets in a zero-shot manner, achieving F1 scores of 82{\%} and 78{\%}, respectively. This result indicates that the quality of our corpus is not less than commonly used datasets. It{\textquoteleft}s a pioneering contribution to the field. We will publicize a subset of 1,800 instances from our corpus, free from any licensing issues.