GenAI4ELab/papercli-papers huggingface dataset:
HFMRE: Constructing Huffman Tree in Bags to Find Excellent Instances for Distantly Supervised Relation Extraction
Authors: Min Li; Cong Shao; Gang Li; Mingle Zhou
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Since the introduction of distantly supervised relation extraction methods, numerous approaches have been developed, the most representative of which is multi-instance learning (MIL). To find reliable features that are most representative of multi-instance bags, aggregation strategies such as AVG (average), ONE (at least one), and ATT (sentence-level attention) are commonly used. These strategies tend to train third-party vectors to select sentence-level features, leaving it to the third party to decide/identify what is noise, ignoring the intrinsic associations that naturally exist from sentence to sentence. In this paper, we propose the concept of circular cosine similarity, which is used to explicitly show the intrinsic associations between sentences within a bag. We also consider the previous methods to be a crude denoising process as they are interrupted and do not have a continuous noise detection procedure. Following this consideration, we implement a relation extraction framework (HFMRE) that relies on the Huffman tree, where sentences are considered as leaf nodes and circular cosine similarity are considered as node weights. HFMRE can continuously and iteratively discriminate noise and aggregated features during the construction of the Huffman tree, eventually finding an excellent instance that is representative of a bag-level feature. The experiments demonstrate the remarkable effectiveness of our method, outperforming previously advanced baselines on the popular DSRE datasets.
DISCO: A Large Scale Human Annotated Corpus for Disfluency Correction in Indo-European Languages
Authors: Vineet Bhat; Preethi Jyothi; Pushpak Bhattacharyya
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Disfluency correction (DC) is the process of removing disfluent elements like fillers, repetitions and corrections from spoken utterances to create readable and interpretable text. DC is a vital post-processing step applied to Automatic Speech Recognition (ASR) outputs, before subsequent processing by downstream language understanding tasks. Existing DC research has primarily focused on English due to the unavailability of large-scale open-source datasets. Towards the goal of multilingual disfluency correction, we present a high-quality human-annotated DC corpus covering four important Indo-European languages: English, Hindi, German and French. We provide extensive analysis of results of state-of-the-art DC models across all four languages obtaining F1 scores of 97.55 (English), 94.29 (Hindi), 95.89 (German) and 92.97 (French). To demonstrate the benefits of DC on downstream tasks, we show that DC leads to 5.65 points increase in BLEU scores on average when used in conjunction with a state-of-the-art Machine Translation (MT) system. We release code to run our experiments along with our annotated dataset here.
Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic Capacity
Authors: Haoran Xu; Maha Elbayad; Kenton Murray; Jean Maillard; Vedanuj Goswami
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Mixture-of-experts (MoE) models that employ sparse activation have demonstrated effectiveness in significantly increasing the number of parameters while maintaining low computational requirements per token. However, recent studies have established that MoE models are inherently parameter-inefficient as the improvement in performance diminishes with an increasing number of experts. We hypothesize this parameter inefficiency is a result of all experts having equal capacity, which may not adequately meet the varying complexity requirements of different tokens or tasks. In light of this, we propose Stratified Mixture of Experts (SMoE) models, which feature a stratified structure and can assign dynamic capacity to different tokens. We demonstrate the effectiveness of SMoE on three multilingual machine translation benchmarks, containing 4, 15, and 94 language pairs, respectively. We show that SMoE outperforms multiple state-of-the-art MoE models with the same or fewer parameters.
Misery Loves Complexity: Exploring Linguistic Complexity in the Context of Emotion Detection
Authors: Pranaydeep Singh; Luna De Bruyne; Orphée De Clercq; Els Lefever
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Given the omnipresence of social media in our society, thoughts and opinions are being shared online in an unprecedented manner. This means that both positive and negative emotions can be equally and freely expressed. However, the negativity bias posits that human beings are inherently drawn to and more moved by negativity and, as a consequence, negative emotions get more traffic. Correspondingly, when writing about emotions this negativity bias could lead to expressions of negative emotions that are linguistically more complex. In this paper, we attempt to use readability and linguistic complexity metrics to better understand the manifestation of emotions on social media platforms like Reddit based on the widely-used GoEmotions dataset. We demonstrate that according to most metrics, negative emotions indeed tend to generate more complex text than positive emotions. In addition, we examine whether a higher complexity hampers the automatic identification of emotions. To answer this question, we fine-tuned three state-of-the-art transformers (BERT, RoBERTa, and SpanBERT) on the same emotion detection dataset. We demonstrate that these models often fail to predict emotions for the more complex texts. More advanced LLMs like RoBERTa and SpanBERT also fail to improve by significant margins on complex samples. This calls for a more nuanced interpretation of the emotion detection performance of transformer models. We make the automatically annotated data available for further research at: https://huggingface.co/datasets/pranaydeeps/CAMEO
Probing the “Creativity” of Large Language Models: Can models produce divergent semantic association?
Authors: Honghua Chen; Nai Ding
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Large language models possess remarkable capacity for processing language, but it remains unclear whether these models can further generate creative content. The present study aims to investigate the creative thinking of large language models through a cognitive perspective. We utilize the divergent association task (DAT), an objective measurement of creativity that asks models to generate unrelated words and calculates the semantic distance between them. We compare the results across different models and decoding strategies. Our findings indicate that: (1) When using the greedy search strategy, GPT-4 outperforms 96% of humans, while GPT-3.5-turbo exceeds the average human level. (2) Stochastic sampling and temperature scaling are effective to obtain higher DAT scores for models except GPT-4, but face a trade-off between creativity and stability. These results imply that advanced large language models have divergent semantic associations, which is a fundamental process underlying creativity.
Code-Switching with Word Senses for Pretraining in Neural Machine Translation
Authors: Vivek Iyer; Edoardo Barba; Alexandra Birch; Jeff Pan; Roberto Navigli
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Lexical ambiguity is a significant and pervasive challenge in Neural Machine Translation (NMT), with many state-of-the-art (SOTA) NMT systems struggling to handle polysemous words (Campolungo et al., 2022). The same holds for the NMT pretraining paradigm of denoising synthetic “code-switched” text (Pan et al., 2021; Iyer et al., 2023), where word senses are ignored in the noising stage – leading to harmful sense biases in the pretraining data that are subsequently inherited by the resulting models. In this work, we introduce Word Sense Pretraining for Neural Machine Translation (WSP-NMT) - an end-to-end approach for pretraining multilingual NMT models leveraging word sense-specific information from Knowledge Bases. Our experiments show significant improvements in overall translation quality. Then, we show the robustness of our approach to scale to various challenging data and resource-scarce scenarios and, finally, report fine-grained accuracy improvements on the DiBiMT disambiguation benchmark. Our studies yield interesting and novel insights into the merits and challenges of integrating word sense information and structured knowledge in multilingual pretraining for NMT.
DiffusionSL: Sequence Labeling via Tag Diffusion Process
Authors: Ziyang Huang; Pengfei Cao; Jun Zhao; Kang Liu
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Sequence Labeling (SL) is long-standing in Natural Language Processing (NLP). Traditionally, discriminative models have been widely used to capture the conditional distribution of sequence tags, rather than generative models. In this paper, we present DiffusionSL, a framework that utilizes a conditional discrete diffusion model for generating discrete tag data, resulting in a Tag Diffusion Process. We treat the natural language sequence as the conditional signal and the sequence tags as the generation target, iteratively refining the noisy tags to obtain clean ones. To address the discreteness issue, we propose the Bit-Tag Converter (BTConverter) to model the target in continuous data space. Furthermore, we introduce the Bit Diffusion Transformer (BitDiT) to model the process of noise elimination. Leveraging the powerful iterative refinement capability of the diffusion model, DiffusionSL achieves superior performance against previous state-of-the-art (SOTA) baselines and outperforms gpt-3.5-turbo significantly across multiple benchmark datasets and various tasks.
COMET-M: Reasoning about Multiple Events in Complex Sentences
Authors: Sahithya Ravi; Raymond Ng; Vered Shwartz
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Understanding the speaker’s intended meaning often involves drawing commonsense inferences to reason about what is not stated explicitly. In multi-event sentences, it requires understanding the relationships between events based on contextual knowledge. We propose COMET-M (Multi-Event), an event-centric commonsense model capable of generating commonsense inferences for a target event within a complex sentence. COMET-M builds upon COMET (Bosselut et al., 2019), which excels at generating event-centric inferences for simple sentences, but struggles with the complexity of multi-event sentences prevalent in natural text. To overcome this limitation, we curate a Multi-Event Inference (MEI) dataset of 35K human-written inferences. We train COMET-M on the human-written inferences and also create baselines using automatically labeled examples. Experimental results demonstrate the significant performance improvement of COMET-M over COMET in generating multi-event inferences. Moreover, COMET-M successfully produces distinct inferences for each target event, taking the complete context into consideration. COMET-M holds promise for downstream tasks involving natural text such as coreference resolution, dialogue, and story understanding.
On Event Individuation for Document-Level Information Extraction
Authors: William Gantt; Reno Kriz; Yunmo Chen; Siddharth Vashishtha; Aaron Steven White
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: As information extraction (IE) systems have grown more adept at processing whole documents, the classic task of *template filling* has seen renewed interest as a benchmark for document-level IE. In this position paper, we call into question the suitability of template filling for this purpose. We argue that the task demands definitive answers to thorny questions of *event individuation* — the problem of distinguishing distinct events — about which even human experts disagree. Through an annotation study and error analysis, we show that this raises concerns about the usefulness of template filling metrics, the quality of datasets for the task, and the ability of models to learn it. Finally, we consider possible solutions.
AniEE: A Dataset of Animal Experimental Literature for Event Extraction
Authors: Dohee Kim; Ra Yoo; Soyoung Yang; Hee Yang; Jaegul Choo
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Event extraction (EE), as a crucial information extraction (IE) task, aims to identify event triggers and their associated arguments from unstructured text, subsequently classifying them into pre-defined types and roles. In the biomedical domain, EE is widely used to extract complex structures representing biological events from literature. Due to the complicated semantics and specialized domain knowledge, it is challenging to construct biomedical event extraction datasets. Additionally, most existing biomedical EE datasets primarily focus on cell experiments or the overall experimental procedures. Therefore, we introduce AniEE, an event extraction dataset concentrated on the animal experiment stage. We establish a novel animal experiment customized entity and event scheme in collaboration with domain experts. We then create an expert-annotated high-quality dataset containing discontinuous entities and nested events and evaluate our dataset on the recent outstanding NER and EE models.
From Words to Wires: Generating Functioning Electronic Devices from Natural Language Descriptions
Authors: Peter Jansen
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: In this work, we show that contemporary language models have a previously unknown skill – the capacity for electronic circuit design from high-level textual descriptions, akin to code generation. We introduce two benchmarks: PINS100, assessing model knowledge of electrical components, and MICRO25, evaluating a model’s capability to design common microcontroller circuits and code in the Arduino ecosystem that involve input, output, sensors, motors, protocols, and logic – with models such as GPT-4 and Claude-V1 achieving between 60% to 96% Pass@1 on generating full devices. We include six case studies of using language models as a design assistant for moderately complex devices, such as a radiation-powered random number generator, an emoji keyboard, a visible spectrometer, and several assistive devices, while offering a qualitative analysis performance, outlining evaluation challenges, and suggesting areas of development to improve complex circuit design and practical utility. With this work, we aim to spur research at the juncture of natural language processing and electronic design.
Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training
Authors: Zhisong Zhang; Emma Strubell; Eduard Hovy
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: In this work we propose a pragmatic method that reduces the annotation cost for structured label spaces using active learning. Our approach leverages partial annotation, which reduces labeling costs for structured outputs by selecting only the most informative sub-structures for annotation. We also utilize self-training to incorporate the current model’s automatic predictions as pseudo-labels for un-annotated sub-structures. A key challenge in effectively combining partial annotation with self-training to reduce annotation cost is determining which sub-structures to select to label. To address this challenge, we adopt an error estimator to adaptively decide the partial selection ratio according to the current model’s capability. In evaluations spanning four structured prediction tasks, we show that our combination of partial annotation and self-training using an adaptive selection ratio reduces annotation cost over strong full annotation baselines under a fair comparison scheme that takes reading time into consideration.
Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading
Authors: Yangyang Luo; Shiyu Tian; Caixia Yuan; Xiaojie Wang
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Conversational Machine Reading (CMR) requires answering a user’s initial question through multi-turn dialogue interactions based on a given document. Although there exist many effective methods, they largely neglected the alignment between the document and the user-provided information, which significantly affects the intermediate decision-making and subsequent follow-up question generation. To address this issue, we propose a pipeline framework that (1) aligns the aforementioned two sides in an explicit way, (2) makes decisions using a lightweight many-to-many entailment reasoning module, and (3) directly generates follow-up questions based on the document and previously asked questions. Our proposed method achieves state-of-the-art in micro-accuracy and ranks the first place on the public leaderboard of the CMR benchmark dataset ShARC.
Harnessing Dataset Cartography for Improved Compositional Generalization in Transformers
Authors: Osman İnce; Tanin Zeraati; Semih Yagcioglu; Yadollah Yaghoobzadeh; Erkut Erdem; Aykut Erdem
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Neural networks have revolutionized language modeling and excelled in various downstream tasks. However, the extent to which these models achieve compositional generalization comparable to human cognitive abilities remains a topic of debate. While existing approaches in the field have mainly focused on novel architectures and alternative learning paradigms, we introduce a pioneering method harnessing the power of dataset cartography (Swayamdipta et al., 2020). By strategically identifying a subset of compositional generalization data using this approach, we achieve a remarkable improvement in model accuracy, yielding enhancements of up to 10% on CFQ and COGS datasets. Notably, our technique incorporates dataset cartography as a curriculum learning criterion, eliminating the need for hyperparameter tuning while consistently achieving superior performance. Our findings highlight the untapped potential of dataset cartography in unleashing the full capabilities of compositional generalization within Transformer models.
Roles of Scaling and Instruction Tuning in Language Perception: Model vs. Human Attention
Authors: Changjiang Gao; Shujian Huang; Jixing Li; Jiajun Chen
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Recent large language models (LLMs) have revealed strong abilities to understand natural language. Since most of them share the same basic structure, i.e. the transformer block, possible contributors to their success in the training process are scaling and instruction tuning. However, how these factors affect the models’ language perception is unclear. This work compares the self-attention of several existing LLMs (LLaMA, Alpaca and Vicuna) in different sizes (7B, 13B, 30B, 65B), together with eye saccade, an aspect of human reading attention, to assess the effect of scaling and instruction tuning on language perception. Results show that scaling enhances the human resemblance and improves the effective attention by reducing the trivial pattern reliance, while instruction tuning does not. However, instruction tuning significantly enhances the models’ sensitivity to instructions. We also find that current LLMs are consistently closer to non-native than native speakers in attention, suggesting a sub-optimal language perception of all models. Our code and data used in the analysis is available on GitHub.
Efficient Data Learning for Open Information Extraction with Pre-trained Language Models
Authors: Zhiyuan Fan; Shizhu He
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Open Information Extraction (OpenIE) is a fundamental yet challenging task in Natural Language Processing, which involves extracting all triples (subject, predicate, object) from a given sentence. While labelling-based methods have their merits, generation-based techniques offer unique advantages, such as the ability to generate tokens not present in the original sentence. However, these generation-based methods often require a significant amount of training data to learn the task form of OpenIE and substantial training time to overcome slow model convergence due to the order penalty. In this paper, we introduce a novel framework, OK-IE, that ingeniously transforms the task form of OpenIE into the pre-training task form of the T5 model, thereby reducing the need for extensive training data. Furthermore, we introduce an innovative concept of ‘anchors’ to control the sequence of model outputs, effectively eliminating the impact of order penalty on model convergence and significantly reducing training time. Experimental results indicate that, compared to previous SOTA methods, OK-IE requires only 1/100 of the training data (900 instances) and 1/120 of the training time (3 minutes) to achieve comparable results.
Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning
Authors: Han Zhou; Xingchen Wan; Ivan Vulić; Anna Korhonen
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Prompt-based learning has been an effective paradigm for large pretrained language models (LLM), enabling few-shot or even zero-shot learning. Black-box prompt search has received growing interest recently for its distinctive properties of gradient-free optimization, proven particularly useful and powerful for model-as-a-service usage. However, the discrete nature and the complexity of combinatorial optimization hinder the efficiency of modern black-box approaches. Despite extensive research on search algorithms, the crucial aspect of search space design and optimization has been largely overlooked. In this paper, we first conduct a sensitivity analysis by prompting LLM, revealing that only a small number of tokens exert a disproportionate amount of influence on LLM predictions. Leveraging this insight, we propose the Clustering and Pruning for Efficient Black-box Prompt Search (ClaPS), a simple black-box search method that first clusters and prunes the search space to focus exclusively on influential prompt tokens. By employing even simple search methods within the pruned search space, ClaPS achieves state-of-the-art performance across various tasks and LLMs, surpassing the performance of complex approaches while significantly reducing search costs. Our findings underscore the critical role of search space design and optimization in enhancing both the usefulness and the efficiency of black-box prompt-based learning.