mtybilly/Instruct2DS huggingface dataset:
MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models
Authors: Yisong Su, Huanhuan Ma, Xiaoyan Yu, Fangyu Lei, Yuanzhe Zhang, Jun Zhao, Kang Liu Yifan Wei
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Large language models (LLMs) have shown nearly saturated performance on many natural language processing (NLP) tasks. As a result, it is natural for people to believe that LLMs have also mastered abilities such as time understanding and reasoning. However, research on the temporal sensitivity of LLMs has been insufficiently emphasized. To fill this gap, this paper constructs Multiple Sensitive Factors Time QA (MenatQA), which encompasses three temporal factors (scope factor, order factor, counterfactual factor) with total 2,853 samples for evaluating the time comprehension and reasoning abilities of LLMs. This paper tests current mainstream LLMs with different parameter sizes, ranging from billions to hundreds of billions. The results show most LLMs fall behind smaller temporal reasoning models with different degree on these factors. In specific, LLMs show a significant vulnerability to temporal biases and depend heavily on the temporal information provided in questions. Furthermore, this paper undertakes a preliminary investigation into potential improvement strategies by devising specific prompts and leveraging external tools. These approaches serve as valuable baselines or references for future research endeavors.
What Makes Chain-of-Thought Prompting Effective? A Counterfactual Study
Authors: Katherine Hermann, Amir Yazdanbakhsh Aman Madaan
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: The effectiveness of Chain-of-thought prompting (CoT) has been widely recognized, but the underlying mechanisms behind its success, the reason why it just works for a wide range of tasks, remains an open question. To investigate this, we employ a counterfactual prompting approach, systematically manipulating elements of examples used in a few-shot prompt, and testing the consequences on model behavior. This allows us to understand the relative contributions of prompt elements such as symbols (digits, entities) and patterns (equations, sentence structure) on in-context learning. Our experiments with three different large language models (LLMs) reveal several key findings. First, the specific symbols used in the prompt do not significantly impact the model`s performance. However, consistent patterns in examples and specifying text in style frequently found on the web are crucial. Second, our findings suggest that the necessity of accurate few-shot examples depends on their role in communicating task understanding. We identify tasks where inaccurate few-shot examples hurt and, surprisingly, tasks where they improve performance. Additionally, we find that the intermediate steps in CoT may not necessarily facilitate learning how to solve a task, but instead efficiently convey task understanding (what) to the model. Furthermore, CoT leverages LLMs to fill in missing commonsense information, particularly helping difficult reasoning problems and long-tail questions.
Perceptual Structure in the absence of grounding: the impact of abstractedness and subjectivity in color language for LLMs
Authors: Edison Marrese-Taylor, Andres Hoyos-Idrobo Pablo Loyola
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: The need for grounding in language understanding is an active research topic. Previous work has suggested that color perception and color language appear as a suitable test bed to empirically study the problem, given its cognitive significance and showing that there is considerable alignment between a defined color space and the feature space defined by a language model. To further study this issue, we collect a large scale source of colors and their descriptions, containing almost a 1 million examples , and perform an empirical analysis to compare two kinds of alignments: (i) inter-space, by learning a mapping between embedding space and color space, and (ii) intra-space, by means of prompting comparatives between color descriptions. Our results show that while color space alignment holds for monolexemic, highly pragmatic color descriptions, this alignment drops considerably in the presence of examples that exhibit elements of real linguistic usage such as subjectivity and abstractedness, suggesting that grounding may be required in such cases.
A Dataset for Investigating the Impact of Context for Offensive Language Detection in Tweets
Authors: Ömer Özdemir, Mustafa Erengül, Arzucan Özgür Musa İhtiyar
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Offensive language detection is crucial in natural language processing (NLP). We investigated the importance of context for detecting such language in reply tweets on Twitter, where the use of offensive language is widespread. We collected a Turkish tweet dataset where the target group was unvaccinated people during the Covid period. Tweets in the dataset were enriched with contextual information by adding the original tweet to which a particular tweet was posted as a reply. The dataset, which includes over 28,000 tweet-reply pairs, was manually labeled by human annotators and made publicly available. In addition, we compared the performance of different machine learning models with and without contextual information. Our results show that this type of contextual information was not very useful in improving the performance of the models in general, although it slightly increased the macro-averaged F1-score of certain models.
Remember what you did so you know what to do next
Authors: Alex Hedges, Yash Kankanampati, Justin Martin, Marjorie Freedman, Ralph Weischedel Manuel Ciosici
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: We explore using the 6B parameter GPT-J language model to create a plan for a simulated robot to achieve 30 classes of goals in ScienceWorld, a text game simulator for elementary science experiments and for which previously published empirical work has shown large language models (LLM)s to be a poor fit (Wang et al., 2022). Using the Markov assumption, the LLM outperforms the state-of-the-art based on reinforcement learning by a factor of 1.4. When we fill the LLM`s input buffer with as many prior steps as will fit, improvement rises to 3.3x. Even when training on only 6.5% of the training data, we observe a 2.3x improvement over the state-of-the-art. Our experiments show that performance varies widely across the 30 classes of actions, indicating that averaging over tasks can hide significant performance issues.
An Empirical Study of Multimodal Model Merging
Authors: Linjie Li, Kevin Lin, Zhe Gan, Mohit Bansal, Lijuan Wang Yi-Lin Sung
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Model merging (e.g., via interpolation or task arithmetic) fuses multiple models trained on different tasks to generate a multi-task solution. The technique has been proven successful in previous studies, where the models are trained on similar tasks and with the same initialization. In this paper, we expand on this concept to a multimodal setup by merging transformers trained on different modalities. Furthermore, we conduct our study for a novel goal where we can merge vision, language, and cross-modal transformers of a modality-specific architecture to create a parameter-efficient modality-agnostic architecture. Through comprehensive experiments, we systematically investigate the key factors impacting model performance after merging, including initialization, merging mechanisms, and model architectures. We also propose two metrics that assess the distance between weights to be merged and can serve as an indicator of the merging outcomes. Our analysis leads to an effective training recipe for matching the performance of the modality-agnostic baseline (i.e., pre-trained from scratch) via model merging. Our method also outperforms naive merging significantly on various tasks, with improvements of 3% on VQA, 7% on COCO retrieval, 25% on NLVR2, 14% on Flickr30k and 3% on ADE20k.
Learning to Abstract with Nonparametric Variational Information Bottleneck
Authors: Fabio Fehr, James Henderson Melika Behjati
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Learned representations at the level of characters, sub-words, words, and sentences, have each contributed to advances in understanding different NLP tasks and linguistic phenomena. However, learning textual embeddings is costly as they are tokenization specific and require different models to be trained for each level of abstraction. We introduce a novel language representation model which can learn to compress to different levels of abstraction at different layers of the same model. We apply Nonparametric Variational Information Bottleneck (NVIB) to stacked Transformer self-attention layers in the encoder, which encourages an information-theoretic compression of the representations through the model. We find that the layers within the model correspond to increasing levels of abstraction and that their representations are more linguistically informed. Finally, we show that NVIB compression results in a model which is more robust to adversarial perturbations.
Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document
Authors: Qian Xiao, Juncheng Li, Duo Dong, Jun Lin, Xiaozhong Liu, Siliang Tang Xiangnan Chen
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Visual Relation Extraction (VRE) is a powerful means of discovering relationships between entities within visually-rich documents. Existing methods often focus on manipulating entity features to find pairwise relations, yet neglect the more fundamental structural information that links disparate entity pairs together. The absence of global structure information may make the model struggle to learn long-range relations and easily predict conflicted results. To alleviate such limitations, we propose a GlObal Structure knowledge-guided relation Extraction (GOSE) framework. GOSE initiates by generating preliminary relation predictions on entity pairs extracted from a scanned image of the document. Subsequently, global structural knowledge is captured from the preceding iterative predictions, which are then incorporated into the representations of the entities. This “generate-capture-incorporate” cycle is repeated multiple times, allowing entity representations and global structure knowledge to be mutually reinforced. Extensive experiments validate that GOSE not only outperforms existing methods in the standard fine-tuning setting but also reveals superior cross-lingual learning capabilities; indeed, even yields stronger data-efficient performance in the low-resource setting.
Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalization
Authors: Shuangtao Li, Yafang Zheng, Biao Fu, Shan Liu, Yidong Chen, Xiaodong Shi Lei Lin
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Recent studies have shown that sequence-to-sequence (seq2seq) models struggle with compositional generalization (CG), i.e., the ability to systematically generalize to unseen compositions of seen components. There is mounting evidence that one of the reasons hindering CG is the representation of the encoder uppermost layer is entangled, i.e., the syntactic and semantic representations of sequences are entangled. However, we consider that the previously identified representation entanglement problem is not comprehensive enough. Additionally, we hypothesize that the source keys and values representations passing into different decoder layers are also entangled. Starting from this intuition, we propose CompoSition (\textbfCompose \textbfSyntactic and Semant\textbfic Representa\textbftions), an extension to seq2seq models which learns to compose representations of different encoder layers dynamically for different tasks, since recent studies reveal that the bottom layers of the Transformer encoder contain more syntactic information and the top ones contain more semantic information. Specifically, we introduce a \textitcomposed layer between the encoder and decoder to compose different encoder layers' representations to generate specific keys and values passing into different decoder layers. CompoSition achieves competitive results on two comprehensive and realistic benchmarks, which empirically demonstrates the effectiveness of our proposal. Codes are available at r̆lhttps://github.com/thinkaboutzero/COMPOSITION.
SelectNoise: Unsupervised Noise Injection to Enable Zero-Shot Machine Translation for Extremely Low-resource Languages
Authors: Kaushal Maurya, Maunendra Desarkar Maharaj Brahma
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: In this work, we focus on the task of machine translation (MT) from extremely low-resource language (ELRLs) to English. The unavailability of parallel data, lack of representation from large multilingual pre-trained models, and limited monolingual data hinder the development of MT systems for ELRLs. However, many ELRLs often share lexical similarities with high-resource languages (HRLs) due to factors such as dialectical variations, geographical proximity, and language structure. We utilize this property to improve cross-lingual signals from closely related HRL to enable MT for ELRLs. Specifically, we propose a novel unsupervised approach, $\textitSelectNoise$, based on $\textitselective candidate extraction$ and $\textitnoise injection$ to generate noisy HRLs training data. The noise injection acts as a regularizer, and the model trained with noisy data learns to handle lexical variations such as spelling, grammar, and vocabulary changes, leading to improved cross-lingual transfer to ELRLs. The selective candidates are extracted using BPE merge operations and edit operations, and noise injection is performed using greedy, top-p, and top-k sampling strategies. We evaluate the proposed model on 12 ELRLs from the FLORES-200 benchmark in a zero-shot setting across two language families. The proposed model outperformed all the strong baselines, demonstrating its efficacy. It has comparable performance with the supervised noise injection model. Our code and model are publicly available.
Breaking Boundaries in Retrieval Systems: Unsupervised Domain Adaptation with Denoise-Finetuning
Authors: Ching-Wen Yang, Chun-Yi Lin, Hung-Yu Kao Che Chen
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Dense retrieval models have exhibited remarkable effectiveness, but they rely on abundant labeled data and face challenges when applied to different domains. Previous domain adaptation methods have employed generative models to generate pseudo queries, creating pseudo datasets to enhance the performance of dense retrieval models. However, these approaches typically use unadapted rerank models, leading to potentially imprecise labels. In this paper, we demonstrate the significance of adapting the rerank model to the target domain prior to utilizing it for label generation. This adaptation process enables us to obtain more accurate labels, thereby improving the overall performance of the dense retrieval model. Additionally, by combining the adapted retrieval model with the adapted rerank model, we achieve significantly better domain adaptation results across three retrieval datasets. We release our code for future research.
Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach
Authors: Jifan Yu, Juanzi Li, Lei Hou Zheyuan Zhang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Large Language Models (LLMs) have not only exhibited exceptional performance across various tasks, but also demonstrated sparks of intelligence. Recent studies have focused on assessing their capabilities on human exams and revealed their impressive competence in different domains. However, cognitive research on the overall knowledge structure of LLMs is still lacking. In this paper, based on educational diagnostic assessment method, we conduct an evaluation using MoocRadar, a meticulously annotated human test dataset based on Bloom Taxonomy. We aim to reveal the knowledge structures of LLMs and gain insights of their cognitive capabilities. This research emphasizes the significance of investigating LLMs' knowledge and understanding the disparate cognitive patterns of LLMs. By shedding light on models' knowledge, researchers can advance development and utilization of LLMs in a more informed and effective manner.
Simpler neural networks prefer subregular languages
Authors: Richard Futrell Charles Torres
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: We apply a continuous relaxation of $L_0$ regularization (Louizos et al., 2017), which induces sparsity, to study the inductive biases of LSTMs. In particular, we are interested in the patterns of formal languages which are readily learned and expressed by LSTMs. Across a wide range of tests we find sparse LSTMs prefer subregular languages over regular languages and the strength of this preference increases as we increase the pressure for sparsity. Furthermore LSTMs which are trained on subregular languages have fewer non-zero parameters. We conjecture that this subregular bias in LSTMs is related to the cognitive bias for subregular language observed in human phonology which are both downstream of a simplicity bias in a suitable description language.
Simple Hardware-Efficient PCFGs with Independent Left and Right Productions
Authors: Songlin Yang, Yoon Kim, Kewei Tu Wei Liu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Scaling dense PCFGs to thousands of nonterminals via low-rank parameterizations of the rule probability tensor has been shown to be beneficial for unsupervised parsing. However, PCFGs scaled this way still perform poorly as a language model, and even underperform similarly-sized HMMs. This work introduces $\emphSimplePCFG$, a simple PCFG formalism with independent left and right productions. Despite imposing a stronger independence assumption than the low-rank approach, we find that this formalism scales more effectively both as a language model and as an unsupervised parser. We further introduce $\emphFlashInside$, a hardware IO-aware implementation of the inside algorithm for efficiently scaling simple PCFGs. Through extensive experiments on multiple grammar induction benchmarks, we validate the effectiveness of simple PCFGs over low-rank baselines.
R$^3$ Prompting: Review, Rephrase and Resolve for Chain-of-Thought Reasoning in Large Language Models under Noisy Context
Authors: Hanlun Zhu, Lei Wang, Yang Li, Yunshi Lan Qingyuan Tian
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: With the help of Chain-of-Thought (CoT) prompting, Large Language Models (LLMs) have achieved remarkable performance on various reasoning tasks. However, most of them have been evaluated under noise-free context and the dilemma for LLMs to produce inaccurate results under the noisy context has not been fully investigated. Existing studies utilize trigger sentences to encourage LLMs to concentrate on the relevant information but the trigger has limited effect on final answer prediction. Inspired by interactive CoT method, where intermediate reasoning steps are promoted by multiple rounds of interaction between users and LLMs, we propose a novel prompting method, namely R$^3$ prompting, for CoT reasoning under noisy context. Specifically, R$^3$ prompting interacts with LLMs to perform key sentence extraction, variable declaration and answer prediction, which corresponds to a thought process of reviewing, rephrasing and resolving. The responses generated at the last interaction will perform as hints to guide toward the responses of the next interaction. Our experiments show that R$^3$ prompting significantly outperforms existing CoT prompting methods on five reasoning tasks under noisy context. With GPT-3.5-turbo, we observe 3.7% accuracy improvement on average on the reasoning tasks under noisy context compared to the most competitive prompting baseline. More analyses and ablation studies show the robustness and generalization of R$^3$ prompting method in solving reasoning tasks in LLMs under noisy context.
Quality Estimation-Assisted Automatic Post-Editing
Authors: Diptesh Kanojia, Fred Blain, Tharindu Ranasinghe, Pushpak Bhattacharyya Sourabh Deoghare
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Automatic Post-Editing (APE) systems are prone to over-correction of the Machine Translation (MT) outputs. While Word-level Quality Estimation (QE) system can provide a way to curtail the over-correction, a significant performance gain has not been observed thus far by utilizing existing APE and QE combination strategies. In this paper, we propose joint training of a model on APE and QE tasks to improve the APE. Our proposed approach utilizes a multi-task learning (MTL) methodology, which shows significant improvement while treating both tasks as a \textquoteleftbargaining game' during training. Moreover, we investigate various existing combination strategies and show that our approach achieves state-of-the-art performance for a \textquoteleftdistant' language pair, viz., English-Marathi. We observe an improvement of 1.09 TER and 1.37 BLEU points over a baseline QE-Unassisted APE system for English-Marathi, while also observing 0.46 TER and 0.62 BLEU points for English-German. Further, we discuss the results qualitatively and show how our approach helps reduce over-correction, thereby improving the APE performance. We also observe that the degree of integration between QE and APE directly correlates with the APE performance gain. We release our code and models publicly.
Adapter Pruning using Tropical Characterization
Authors: Tushar Vaidya, Soujanya Poria Rishabh Bhardwaj
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Adapters are widely popular parameter-efficient transfer learning approaches in natural language processing that insert trainable modules in between layers of a pre-trained language model. Apart from several heuristics, however, there has been a lack of studies analyzing the optimal number of adapter parameters needed for downstream applications. Thus, we propose an adapter pruning approach by studying the tropical characteristics of trainable modules. We cast it as an optimization problem that aims to prune parameters from the adapter layers without changing the orientation of underlying tropical hypersurfaces. Our experiments on five NLP datasets show that tropical geometry tends to identify more relevant parameters to prune when compared with the magnitude-based baseline, while a combined approach works best across the tasks.
Self-Supervised Rule Learning to Link Text Segments to Relational Elements of Structured Knowledge
Authors: Udit Sharma, Hima Karanam, Sumit Neelam, Ronny Luss, Dheeraj Sreedhar, Pavan Kapanipathi, Naweed Khan, Kyle Erwin, Ndivhuwo Makondo, Ibrahim Abdelaziz, Achille Fokoue, Alexander Gray, Maxwell Crouse, Subhajit Chaudhury, Chitra Subramanian Shajith Ikbal
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: We present a neuro-symbolic approach to self-learn rules that serve as interpretable knowledge to perform relation linking in knowledge base question answering systems. These rules define natural language text predicates as a weighted mixture of knowledge base paths. The weights learned during training effectively serve the mapping needed to perform relation linking. We use popular masked training strategy to self-learn the rules. A key distinguishing aspect of our work is that the masked training operate over logical forms of the sentence instead of their natural language text form. This offers opportunity to extract extended context information from the structured knowledge source and use that to build robust and human readable rules. We evaluate accuracy and usefulness of such learned rules by utilizing them for prediction of missing kinship relation in CLUTRR dataset and relation linking in a KBQA system using SWQ-WD dataset. Results demonstrate the effectiveness of our approach - its generalizability, interpretability and ability to achieve an average performance gain of 17% on CLUTRR dataset.
TaTA: A Multilingual Table-to-Text Dataset for African Languages
Authors: Sebastian Ruder, Vitaly Nikolaev, Jan Botha, Michael Chavinda, Ankur Parikh, Clara Rivera Sebastian Gehrmann
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Existing data-to-text generation datasets are mostly limited to English. To address this lack of data, we create Table-to-Text in African languages (TaTA), the first large multilingual table-to-text dataset with a focus on African languages. We created TaTA by transcribing figures and accompanying text in bilingual reports by the Demographic and Health Surveys Program, followed by professional translation to make the dataset fully parallel. TaTA includes 8,700 examples in nine languages including four African languages (Hausa, Igbo, Swahili, and Yorùbá) and a zero-shot test language (Russian). We additionally release screenshots of the original figures for future research on multilingual multi-modal approaches. Through an in-depth human evaluation, we show that TaTA is challenging for current models and that less than half the outputs from an mT5-XXL-based model are understandable and attributable to the source data. Our results highlight a) the need for validating metrics; and b) the importance of domain-specific metrics.
Explain-then-translate: an analysis on improving program translation with self-generated explanations
Authors: Mayank Agarwal, Alexander Shypula, Bailin Wang, Derry Wijaya, Jie Chen, Yoon Kim Zilu Tang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: This work explores the use of self-generated natural language explanations as an intermediate step for code-to-code translation with language models. Across three types of explanations and 19 programming languages constructed from the MultiPL-E dataset, we find the explanations to be particularly effective in the zero-shot case, improving performance by 12% on average. Improvements with natural language explanations are particularly pronounced on difficult programs. We release our dataset, code, and canonical solutions in all 19 languages.
Can Brain Signals Reveal Inner Alignment with Human Languages?
Authors: William Han, Jiacheng Zhu, Mengdi Xu, Douglas Weber, Bo Li, Ding Zhao Jielin Qiu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Brain Signals, such as Electroencephalography (EEG), and human languages have been widely explored independently for many downstream tasks, however, the connection between them has not been well explored. In this study, we explore the relationship and dependency between EEG and language. To study at the representation level, we introduced \textbfMTAM, a \textbfMultimodal \textbfTransformer \textbfAlignment \textbfModel, to observe coordinated representations between the two modalities. We used various relationship alignment-seeking techniques, such as Canonical Correlation Analysis and Wasserstein Distance, as loss functions to transfigure features. On downstream applications, sentiment analysis and relation detection, we achieved new state-of-the-art results on two datasets, ZuCo and K-EmoCon. Our method achieved an F1-score improvement of 1.7% on K-EmoCon and 9.3% on Zuco datasets for sentiment analysis, and 7.4% on ZuCo for relation detection. In addition, we provide interpretations of the performance improvement: (1) feature distribution shows the effectiveness of the alignment module for discovering and encoding the relationship between EEG and language; (2) alignment weights show the influence of different language semantics as well as EEG frequency features; (3) brain topographical maps provide an intuitive demonstration of the connectivity in the brain regions. Our code is available at r̆lhttps://github.com/Jason-Qiu/EEG_Language_Alignment.
DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction
Authors: Xiaocheng Gong, Xinjie Yang, Guanting Dong, Shudong Lu, Si Li Gang Zhao
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Most current Event Extraction (EE) methods focus on the high-resource scenario, which requires a large amount of annotated data and can hardly be applied to low-resource domains. To address EE more effectively with limited resources, we propose the Demonstration-enhanced Schema-guided Generation (DemoSG) model, which benefits low-resource EE from two aspects: Firstly, we propose the demonstration-based learning paradigm for EE to fully use the annotated data, which transforms them into demonstrations to illustrate the extraction process and help the model learn effectively. Secondly, we formulate EE as a natural language generation task guided by schema-based prompts, thereby leveraging label semantics and promoting knowledge transfer in low-resource scenarios. We conduct extensive experiments under in-domain and domain adaptation low-resource settings on three datasets, and study the robustness of DemoSG. The results show that DemoSG significantly outperforms current methods in low-resource scenarios.
GLGR: Question-aware Global-to-Local Graph Reasoning for Multi-party Dialogue Reading Comprehension
Authors: Bowei Zou, Yifan Fan, Xibo Li, Ai Ti Aw, Yu Hong Yanling Li
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Graph reasoning contributes to the integration of discretely-distributed attentive information (clues) for Multi-party Dialogue Reading Comprehension (MDRC). This is attributed primarily to multi-hop reasoning over global conversational structures. However, existing approaches barely apply questions for anti-noise graph reasoning. More seriously, the local semantic structures in utterances are neglected, although they are beneficial for bridging across semantically-related clues. In this paper, we propose a question-aware global-to-local graph reasoning approach. It expands the canonical Interlocutor-Utterance graph by introducing a question node, enabling comprehensive global graph reasoning. More importantly, it constructs a semantic-role graph for each utterance, and accordingly performs local graph reasoning conditioned on the semantic relations. We design a two-stage encoder network to implement the progressive reasoning from the global graph to local. The experiments on the benchmark datasets Molweni and FriendsQA show that our approach yields significant improvements, compared to BERT and ELECTRA baselines. It achieves 73.6% and 77.2% F1-scores on Molweni and FriendsQA, respectively, outperforming state-of-the-art methods that employ different pretrained language models as backbones.
Towards Mitigating LLM Hallucination via Self Reflection
Authors: Tiezheng Yu, Yan Xu, Nayeon Lee, Etsuko Ishii, Pascale Fung Ziwei Ji
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Large language models (LLMs) have shown promise for generative and knowledge-intensive tasks including question-answering (QA) tasks. However, the practical deployment still faces challenges, notably the issue of “hallucination”, where models generate plausible-sounding but unfaithful or nonsensical information. This issue becomes particularly critical in the medical domain due to the uncommon professional concepts and potential social risks involved. This paper analyses the phenomenon of hallucination in medical generative QA systems using widely adopted LLMs and datasets. Our investigation centers on the identification and comprehension of common problematic answers, with a specific emphasis on hallucination. To tackle this challenge, we present an interactive self-reflection methodology that incorporates knowledge acquisition and answer generation. Through this feedback process, our approach steadily enhances the factuality, consistency, and entailment of the generated answers. Consequently, we harness the interactivity and multitasking ability of LLMs and produce progressively more precise and accurate answers. Experimental results on both automatic and human evaluation demonstrate the superiority of our approach in hallucination reduction compared to baselines.
Making Body Movement in Sign Language Corpus Accessible for Linguists and Machines with Three-Dimensional Normalization of MediaPipe
Authors: Mayumi Bono Victor Skobov
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Linguists can access movement in the sign language video corpus through manual annotation or computational methods. The first relies on a predefinition of features, and the second requires technical knowledge. Methods like MediaPipe and OpenPose are now more often used in sign language processing. MediaPipe detects a two-dimensional (2D) body pose in a single image with a limited approximation of the depth coordinate. Such 2D projection of a three-dimensional (3D) body pose limits the potential application of the resulting models outside the capturing camera settings and position. 2D pose data does not provide linguists with direct and human-readable access to the collected movement data. We propose our four main contributions: A novel 3D normalization method for MediaPipe`s 2D pose, a novel human-readable way of representing the 3D normalized pose data, an analysis of Japanese Sign Language (JSL) sociolinguistic features using the proposed techniques, where we show how an individual signer can be identified based on unique personal movement patterns suggesting a potential threat to anonymity. Our method outperforms the common 2D normalization on a small, diverse JSL dataset. We demonstrate its benefit for deep learning approaches by significantly outperforming the pose-based state-of-the-art models on the open sign language recognition benchmark.
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
Authors: Jonathan H. Clark, Alexander Gutkin, Mihir Kale, Min Ma, Massimo Nicosia, Shruti Rijhwani, Parker Riley, Jean-Michel A- Sarr, Xinyi Wang, John Wieting, Nitish Gupta, Anna Katanova, Christo Kirov, Dana L. Dickinson, Brian Roark, Bidisha Samanta, Connie Tao, David I. Adelani, Vera Axelrod, Isaac Caswell, Colin Cherry, Dan Garrette, Reeve Ingle, Melvin Johnson, Dmitry Panteleev, Partha Talukdar Sebastian Ruder
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) --- languages for which NLP research is particularly far behind in meeting user needs --- it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather than zero-shot; its focus on user-centric tasks --- tasks with broad adoption by speakers of high-resource languages; and its focus on under-represented languages where this scarce-data scenario tends to be most realistic. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks that are of general utility. We create new datasets for OCR, autocomplete, semantic parsing, and transliteration, and build on and refine existing datasets for other tasks. XTREME-UP provides methodology for evaluating many modeling scenarios including text only, multi-modal (vision, audio, and text), supervised parameter tuning, and in-context learning. We evaluate commonly used models on the benchmark. We release all code and scripts to train and evaluate models.
DiffuVST: Narrating Fictional Scenes with Global-History-Guided Denoising Models
Authors: Mei Yuan, Qi Su Shengguang Wu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Recent advances in image and video creation, especially AI-based image synthesis, have led to the production of numerous visual scenes that exhibit a high level of abstractness and diversity. Consequently, Visual Storytelling (VST), a task that involves generating meaningful and coherent narratives from a collection of images, has become even more challenging and is increasingly desired beyond real-world imagery. While existing VST techniques, which typically use autoregressive decoders, have made significant progress, they suffer from low inference speed and are not well-suited for synthetic scenes. To this end, we propose a novel diffusion-based system DiffuVST, which models the generation of a series of visual descriptions as a single conditional denoising process. The stochastic and non-autoregressive nature of DiffuVST at inference time allows it to generate highly diverse narratives more efficiently. In addition, DiffuVST features a unique design with bi-directional text history guidance and multimodal adapter modules, which effectively improve inter-sentence coherence and image-to-text fidelity. Extensive experiments on the story generation task covering four fictional visual-story datasets demonstrate the superiority of DiffuVST over traditional autoregressive models in terms of both text quality and inference speed.
DiFair: A Benchmark for Disentangled Assessment of Gender Knowledge and Bias
Authors: Kaveh Miandoab, Mohammad Pilehvar Mahdi Zakizadeh
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Numerous debiasing techniques have been proposed to mitigate the gender bias that is prevalent in pretrained language models. These are often evaluated on datasets that check the extent to which the model is gender-neutral in its predictions. Importantly, this evaluation protocol overlooks the possible adverse impact of bias mitigation on useful gender knowledge. To fill this gap, we propose **DiFair**, a manually curated dataset based on masked language modeling objectives. **DiFair** allows us to introduce a unified metric, *gender invariance score*, that not only quantifies a model`s biased behavior, but also checks if useful gender knowledge is preserved. We use **DiFair** as a benchmark for a number of widely-used pretained language models and debiasing techniques. Experimental results corroborate previous findings on the existing gender biases, while also demonstrating that although debiasing techniques ameliorate the issue of gender bias, this improvement usually comes at the price of lowering useful gender knowledge of the model.
Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens
Authors: William Schuler Byung-Doh Oh
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Recent psycholinguistic studies have drawn conflicting conclusions about the relationship between the quality of a language model and the ability of its surprisal estimates to predict human reading times, which has been speculated to be due to the large gap in both the amount of training data and model capacity across studies. The current work aims to consolidate these findings by evaluating surprisal estimates from Transformer-based language model variants that vary systematically in the amount of training data and model capacity on their ability to predict human reading times. The results show that surprisal estimates from most variants with contemporary model capacities provide the best fit after seeing about two billion training tokens, after which they begin to diverge from humanlike expectations. Additionally, newly-trained smaller model variants reveal a \textquotelefttipping point' at convergence, after which the decrease in language model perplexity begins to result in poorer fits to human reading times. These results suggest that the massive amount of training data is mainly responsible for the poorer fit achieved by surprisal from larger pre-trained language models, and that a certain degree of model capacity is necessary for Transformer-based language models to capture humanlike expectations.
ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination
Authors: Jindi Yu, Baotian Hu, Zhenran Xu, Min Zhang Dongfang Li
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: In the field of Large Language Models (LLMs), researchers are increasingly exploring their effectiveness across a wide range of tasks. However, a critical area that requires further investigation is the interpretability of these models, particularly the ability to generate rational explanations for their decisions. Most existing explanation datasets are limited to the English language and the general domain, which leads to a scarcity of linguistic diversity and a lack of resources in specialized domains, such as medical. To mitigate this, we propose ExplainCPE, a challenging medical dataset consisting of over 7K problems from Chinese Pharmacist Examination, specifically tailored to assess the model-generated explanations. From the overall results, only GPT-4 passes the pharmacist examination with a 75.7% accuracy, while other models like ChatGPT fail. Further detailed analysis of LLM-generated explanations reveals the limitations of LLMs in understanding medical text and executing computational reasoning. With the increasing importance of AI safety and trustworthiness, ExplainCPE takes a step towards improving and evaluating the interpretability of LLMs in the medical domain.
CLASS: A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science principles
Authors: Naiming Liu, Debshila Mallick, Richard Baraniuk Shashank Sonkar
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: We present a design framework called Conversational Learning with Analytical Step-by-Step Strategies (CLASS) for building advanced Intelligent Tutoring Systems (ITS) powered by high-performance Large Language Models (LLMs). The CLASS framework empowers ITS with two key capabilities. First, through a carefully curated scaffolding dataset, CLASS equips ITS with essential problem-solving strategies, enabling it to provide tutor-like, step-by-step guidance to students. Second, by using a dynamic conversational dataset, CLASS assists ITS in facilitating natural language interactions, fostering engaging student-tutor conversations. The CLASS framework also provides valuable insights into ITS`s internal decision-making process which allows seamless integration of user feedback, thus enabling continuous refinement and improvement. We also present a proof-of-concept ITS, referred to as SPOCK, which is trained using the CLASS framework with a focus on introductory college level biology content. A carefully constructed protocol was developed for SPOCK`s preliminary evaluation, examining aspects such as the factual accuracy and relevance of its responses. Experts in the field of biology offered favorable remarks, particularly highlighting SPOCK`s capability to break down questions into manageable subproblems and provide encouraging responses to students.
Normal-Abnormal Decoupling Memory for Medical Report Generation
Authors: Yan Yan, Zijian Zhao Guosheng Zhao
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: The automatic generation of medical reports plays a crucial role in clinical automation. In contrast to natural images, radiological images exhibit a high degree of similarity, while medical data are prone to data bias and complex noise, posing challenges for existing methods in capturing nuanced visual information. To address these challenges, we introduce a novel normal-abnormal semantic decoupling network that utilizes abnormal pattern memory. Different from directly optimizing the network using medical reports, we optimize visual extraction through the extraction of abnormal semantics from the reports. Moreover, we independently learn normal semantics based on abnormal semantics, ensuring that the optimization of the visual network remains unaffected by normal semantics learning. Then, we divided the words in the report into four parts: normal/abnormal sentences and normal/abnormal semantics, optimizing the network with distinct weights for each partition. The two semantic components, along with visual information, are seamlessly integrated to facilitate the generation of precise and coherent reports. This approach mitigates the impact of noisy normal semantics and reports. Moreover, we develop a novel encoder for abnormal pattern memory, which improves the network`s ability to detect anomalies by capturing and embedding the abnormal patterns of images in the visual encoder. This approach demonstrates excellent performance on the benchmark MIMIC-CXR, surpassing the current state-of-the-art methods.
mmT5: Modular Multilingual Pre-Training Solves Source Language Hallucinations
Authors: Francesco Piccinno, Massimo Nicosia, Xinyi Wang, Machel Reid, Sebastian Ruder Jonas Pfeiffer
Published in: Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings Track), 2023

Abstract: Multilingual sequence-to-sequence models perform poorly with increased language coverage and fail to consistently generate text in the correct target language in few-shot settings. To address these challenges, we propose mmT5, a modular multilingual sequence-to-sequence model. mmT5 utilizes language-specific modules during pre-training, which disentangle language-specific information from language-agnostic information. We identify representation drift during fine-tuning as a key limitation of modular generative models and develop strategies that enable effective zero-shot transfer. Our model outperforms mT5 at the same parameter sizes by a large margin on representative natural language understanding and generation tasks in 40+ languages. Compared to mT5, mmT5 raises the rate of generating text in the correct language under zero-shot settings from 7% to 99%, thereby greatly alleviating the source language hallucination problem.