GenAI4ELab/papercli-papers huggingface dataset:
Are Language Models Worse than Humans at Following Prompts? It’s Complicated
Authors: Albert Webson; Alyssa Loo; Qinan Yu; Ellie Pavlick
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Prompts have been the center of progress in advancing language models’ zero-shot and few-shot performance. However, recent work finds that models can perform surprisingly well when given intentionally irrelevant or misleading prompts. Such results may be interpreted as evidence that model behavior is not “human like’. In this study, we challenge a central assumption in such work: that humans would perform badly when given pathological instructions. We find that humans are able to reliably ignore irrelevant instructions and thus, like models, perform well on the underlying task despite an apparent lack of signal regarding the task they are being asked to do. However, when given deliberately misleading instructions, humans follow the instructions faithfully, whereas models do not. Thus, our conclusion is mixed with respect to prior work. We argue against the earlier claim that high performance with irrelevant prompts constitutes evidence against models’ instruction understanding, but we reinforce the claim that models’ failure to follow misleading instructions raises concerns. More broadly, we caution that future research should not idealize human behaviors as a monolith and should not train or evaluate models to mimic assumptions about these behaviors without first validating humans’ behaviors empirically.
A Sequence-to-Structure Approach to Document-level Targeted Sentiment Analysis
Authors: Nan Song; Hongjie Cai; Rui Xia; Jianfei Yu; Zhen Wu; Xinyu Dai
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Most previous studies on aspect-based sentiment analysis (ABSA) were carried out at the sentence level, while the research of document-level ABSA has not received enough attention. In this work, we focus on the document-level targeted sentiment analysis task, which aims to extract the opinion targets consisting of multi-level entities from a review document and predict their sentiments. We propose a Sequence-to-Structure (Seq2Struct) approach to address the task, which is able to explicitly model the hierarchical structure among multiple opinion targets in a document, and capture the long-distance dependencies among affiliated entities across sentences. In addition to the existing Seq2Seq approach, we further construct four strong baselines with different pretrained models. Experimental results on six domains show that our Seq2Struct approach outperforms all the baselines significantly. Aside from the performance advantage in outputting the multi-level target-sentiment pairs, our approach has another significant advantage - it can explicitly display the hierarchical structure of the opinion targets within a document. Our source code is publicly released at https://github.com/NUSTM/Doc-TSA-Seq2Struct.
Generating Extractive Answers: Gated Recurrent Memory Reader for Conversational Question Answering
Authors: Xuanyu Zhang; Qing Yang
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Conversational question answering (CQA) is a more complicated task than traditional single-turn machine reading comprehension (MRC). Different from large language models (LLMs) like ChatGPT, the models of CQA need to extract answers from given contents to answer follow-up questions according to conversation history. In this paper, we propose a novel architecture, i.e., Gated Recurrent Memory Reader (GRMR), which integrates traditional extractive MRC models into a generalized sequence-to-sequence framework. After the passage is encoded, the decoder will generate the extractive answers turn by turn. Different from previous models that concatenate the previous questions and answers as context superficially and redundantly, our model can use less storage space and consider historical memory deeply and selectively. Experiments on the Conversational Question Answering (CoQA) dataset show that our model achieves comparable results to most models with the least space occupancy.
Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification
Authors: Jiahuan Yan; Haojun Gao; Zhang Kai; Weize Liu; Danny Chen; Jian Wu; Jintai Chen
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Deep learning approaches exhibit promising performances on various text tasks. However, they are still struggling on medical text classification since samples are often extremely imbalanced and scarce. Different from existing mainstream approaches that focus on supplementary semantics with external medical information, this paper aims to rethink the data challenges in medical texts and present a novel framework-agnostic algorithm called Text2Tree that only utilizes internal label hierarchy in training deep learning models. We embed the ICD code tree structure of labels into cascade attention modules for learning hierarchy-aware label representations. Two new learning schemes, Similarity Surrogate Learning (SSL) and Dissimilarity Mixup Learning (DML), are devised to boost text classification by reusing and distinguishing samples of other labels following the label representation hierarchy, respectively. Experiments on authoritative public datasets and real-world medical records show that our approach stably achieves superior performances over classical and advanced imbalanced classification methods. Our code is available at https://github.com/jyansir/Text2Tree.
Impact of Co-occurrence on Factual Knowledge of Large Language Models
Authors: Cheongwoong Kang; Jaesik Choi
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Large language models (LLMs) often make factually incorrect responses despite their success in various applications. In this paper, we hypothesize that relying heavily on simple co-occurrence statistics of the pre-training corpora is one of the main factors that cause factual errors. Our results reveal that LLMs are vulnerable to the co-occurrence bias, defined as preferring frequently co-occurred words over the correct answer. Consequently, LLMs struggle to recall facts whose subject and object rarely co-occur in the pre-training dataset although they are seen during finetuning. We show that co-occurrence bias remains despite scaling up model sizes or finetuning. Therefore, we suggest finetuning on a debiased dataset to mitigate the bias by filtering out biased samples whose subject-object co-occurrence count is high. Although debiased finetuning allows LLMs to memorize rare facts in the training set, it is not effective in recalling rare facts unseen during finetuning. Further research in mitigation will help build reliable language models by preventing potential errors. The code is available at https://github.com/CheongWoong/impact_of_cooccurrence.
CTQScorer: Combining Multiple Features for In-context Example Selection for Machine Translation
Authors: Aswanth Kumar; Ratish Puduppully; Raj Dabre; Anoop Kunchukuttan
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Large language models have demonstrated the capability to perform on machine translation when the input is prompted with a few examples (in-context learning). Translation quality depends on various features of the selected examples, such as their quality and relevance, but previous work has predominantly focused on individual features in isolation. In this paper, we propose a general framework for combining different features influencing example selection. We learn a regression model, CTQ Scorer (Contextual Translation Quality), that selects examples based on multiple features in order to maximize the translation quality. On multiple language pairs and language models, we show that CTQ Scorer helps significantly outperform random selection as well as strong single-factor baselines reported in the literature. We also see an improvement of over 2.5 COMET points on average with respect to a strong BM25 retrieval-based baseline.
Swap and Predict – Predicting the Semantic Changes in Words across Corpora by Context Swapping
Authors: Taichi Aida; Danushka Bollegala
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Meanings of words change over time and across domains. Detecting the semantic changes of words is an important task for various NLP applications that must make time-sensitive predictions. We consider the problem of predicting whether a given target word, w, changes its meaning between two different text corpora, 𝒞1 and 𝒞2. For this purpose, we propose Swapping-based Semantic Change Detection (SSCD), an unsupervised method that randomly swaps contexts between 𝒞1 and 𝒞2 where w occurs. We then look at the distribution of contextualised word embeddings of w, obtained from a pretrained masked language model (MLM), representing the meaning of w in its occurrence contexts in 𝒞1 and 𝒞2. Intuitively, if the meaning of w does not change between 𝒞1 and 𝒞2, we would expect the distributions of contextualised word embeddings of w to remain the same before and after this random swapping process. Despite its simplicity, we demonstrate that even by using pretrained MLMs without any fine-tuning, our proposed context swapping method accurately predicts the semantic changes of words in four languages (English, German, Swedish, and Latin) and across different time spans (over 50 years and about five years). Moreover, our method achieves significant performance improvements compared to strong baselines for the English semantic change prediction task. Source code is available at https://github.com/a1da4/svp-swap .
Beyond Layout Embedding: Layout Attention with Gaussian Biases for Structured Document Understanding
Authors: Xi Zhu; Xue Han; Shuyuan Peng; Shuo Lei; Chao Deng; Junlan Feng
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Effectively encoding layout information is a central problem in structured document understanding. Most existing methods rely heavily on millions of trainable parameters to learn the layout features of each word from Cartesian coordinates. However, two unresolved questions remain: (1) Is the Cartesian coordinate system the optimal choice for layout modeling? (2) Are massive learnable parameters truly necessary for layout representation? In this paper, we address these questions by proposing Layout Attention with Gaussian Biases (LAGaBi): Firstly, we find that polar coordinates provide a superior choice over Cartesian coordinates as they offer a measurement of both distance and angle between word pairs, capturing relative positions more effectively. Furthermore, by feeding the distances and angles into 2-D Gaussian kernels, we model intuitive inductive layout biases, i.e., the words closer within a document should receive more attention, which will act as the attention biases to revise the textual attention distribution. LAGaBi is model-agnostic and language-independent, which can be applied to a range of transformer-based models, such as the text pre-training models from the BERT series and the LayoutLM series that incorporate visual features. Experimental results on three widely used benchmarks demonstrate that, despite reducing the number of layout parameters from millions to 48, LAGaBi achieves competitive or even superior performance.
ESPVR: Entity Spans Position Visual Regions for Multimodal Named Entity Recognition
Authors: Xiujiao Li; Guanglu Sun; Xinyu Liu
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Multimodal Named Entity Recognition (MNER) uses visual information to improve the performance of text-only Named Entity Recognition (NER). However, existing methods for acquiring local visual information suffer from certain limitations: (1) using an attention-based method to extract visual regions related to the text from visual regions obtained through convolutional architectures (e.g., ResNet), attention is distracted by the entire image, rather than being fully focused on the visual regions most relevant to the text; (2) using an object detection-based (e.g., Mask R-CNN) method to detect visual object regions related to the text, object detection has a limited range of recognition categories. Moreover, the visual regions obtained by object detection may not correspond to the entities in the text. In summary, the goal of these methods is not to extract the most relevant visual regions for the entities in the text. The visual regions obtained by these methods may be redundant or insufficient for the entities in the text. In this paper, we propose an Entity Spans Position Visual Regions (ESPVR) module to obtain the most relevant visual regions corresponding to the entities in the text. Experiments show that our proposed approach can achieve the SOTA on Twitter-2017 and competitive results on Twitter-2015.
Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency
Authors: Lingfeng Shen; Weiting Tan; Boyuan Zheng; Daniel Khashabi
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: With growing capabilities of large language models, prompting them has become the dominant way to access them. This has motivated the development of strategies for automatically selecting effective language prompts. In this paper, we introduce **pFlat** (prompt flatness), a new metric to quantify the expected utility of a language prompt. This metric is inspired by *flatness* regularization in statistical learning that quantifies the robustness of the model towards its parameter perturbations. We provide theoretical foundations for this metric and its relationship with other prompt selection metrics, providing a comprehensive understanding of existing methods. Empirically, we show that combining **pFlat** with existing metrics improves both performance and sample efficiency. Our metric outperforms the previous prompt selection metrics with an average increase of 10% in Pearson correlation across 6 classification benchmarks, and the prompt selected by our metric gains 5% higher accuracy than previous metrics across the benchmarks.
Detecting Erroneously Recognized Handwritten Byzantine Text
Authors: John Pavlopoulos; Vasiliki Kougia; Paraskevi Platanou; Holger Essler
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Handwritten text recognition (HTR) yields textual output that comprises errors, which are considerably more compared to that of recognised printed (OCRed) text. Post-correcting methods can eliminate such errors but may also introduce errors. In this study, we investigate the issues arising from this reality in Byzantine Greek. We investigate the properties of the texts that lead post-correction systems to this adversarial behaviour and we experiment with text classification systems that learn to detect incorrect recognition output. A large masked language model, pre-trained in modern and fine-tuned in Byzantine Greek, achieves an Average Precision score of 95%. The score improves to 97% when using a model that is pre-trained in modern and then in ancient Greek, the two language forms Byzantine Greek combines elements from. A century-based analysis shows that the advantage of the classifier that is further-pre-trained in ancient Greek concerns texts of older centuries. The application of this classifier before a neural post-corrector on HTRed text reduced significantly the post-correction mistakes.
Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment
Authors: Boyang Xue; Weichao Wang; Hongru Wang; Fei Mi; Rui Wang; Yasheng Wang; Lifeng Shang; Xin Jiang; Qun Liu; Kam-Fai Wong
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Pretrained language models (PLMs) based knowledge-grounded dialogue systems are prone to generate responses that are factually inconsistent with the provided knowledge source. In such inconsistent responses, the dialogue models fail to accurately express the external factual knowledge they rely upon. Inspired by previous work which identified that feedforward networks (FFNs) within Transformers are responsible for factual knowledge expressions, we investigate two methods to efficiently improve the factual expression capability of FFNs by knowledge enhancement and alignment respectively. We first propose K-Dial, which explicitly introduces extended FFNs in Transformers to enhance factual knowledge expressions given the specific patterns of knowledge-grounded dialogue inputs. Additionally, we apply the reinforcement learning for factual consistency (RLFC) method to implicitly adjust FFNs’ expressions in responses by aligning with gold knowledge for the factual consistency preference. To comprehensively assess the factual consistency and dialogue quality of responses, we employ extensive automatic measures and human evaluations including sophisticated fine-grained NLI-based metrics. Experimental results on WoW and CMU_DoG datasets demonstrate that our methods efficiently enhance the ability of the FFN module to convey factual knowledge, validating the efficacy of improving factual consistency for knowledge-grounded dialogue systems.
TRIP: Accelerating Document-level Multilingual Pre-training via Triangular Document-level Pre-training on Parallel Data Triplets
Authors: Hongyuan Lu; Haoyang Huang; Shuming Ma; Dongdong Zhang; Wai Lam; Zhaochuan Gao; Anthony Aue; Arul Menezes; Furu Wei
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Despite the success of multilingual sequence-to-sequence pre-training, most existing approaches rely on document-level monolingual corpora in many different languages, sentence-level bilingual corpora, and sometimes synthetic document-level bilingual corpora. This hampers the performance with cross-lingual document-level tasks such as document-level translation. Hence, we propose to mine and leverage document-level trilingual parallel corpora to improve sequence-to-sequence multilingual pre-training. We present Triangular Document-level Pre-training (TRIP) as the first in the field to accelerate the conventional monolingual and bilingual objectives into a trilingual objective with a novel method called Grafting. Experiments show that TRIP achieves several strong state-of-the-art (SOTA) scores on three multilingual document-level machine translation benchmarks and one cross-lingual abstractive summarization benchmark, including consistent improvements by up to 3.11 d-BLEU points and 8.9 ROUGE-L points.
Frequency Balanced Datasets Lead to Better Language Models
Authors: Rodolfo Zevallos; Mireia Farrús; Núria Bel
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: This paper reports on the experiments aimed to improve our understanding of the role of the amount of data required for training attention-based transformer language models. Specifically, we investigate the impact of reducing the immense amounts of required pre-training data through sampling strategies that identify and reduce high-frequency tokens as different studies have indicated that the existence of very high-frequency tokens in pre-training data might bias learning, causing undesired effects. In this light, we describe our sampling algorithm that iteratively assesses token frequencies and removes sentences that contain still high-frequency tokens, eventually delivering a balanced, linguistically correct dataset. We evaluate the results in terms of model perplexity and fine-tuning linguistic probing tasks, NLP downstream tasks as well as more semantic SuperGlue tasks. The results show that pre-training with the resulting balanced dataset allows reducing up to three times the pre-training data.
Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding
Authors: Jianing Wang; Qiushi Sun; Nuo Chen; Chengyu Wang; Jun Huang; Ming Gao; Xiang Li
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: The recent success of large pre-trained language models (PLMs) heavily hinges on massive labeled data, which typically produces inferior performance in low-resource scenarios. To remedy this dilemma, we study self-training as one of the predominant semi-supervised learning (SSL) approaches, which utilizes large-scale unlabeled data to generate synthetic examples. However, too many noisy labels will hurt the model performance, and the self-training procedure requires multiple training iterations making it more expensive if all the model parameters of the PLM are updated. This paper presents UPET, a novel Uncertainty-aware Parameter-Efficient self-Training framework to effectively and efficiently address the labeled data scarcity issue. Specifically, we incorporate Monte Carlo (MC) dropout in Bayesian neural network (BNN) to perform uncertainty estimation for the teacher model and then judiciously select reliable pseudo-labeled examples based on confidence and certainty. During the student training, we introduce multiple parameter-efficient learning (PEL) paradigms that allow optimizes only a small percentage of parameters. We also propose a novel Easy-Hard Contrastive Tuning to enhance the robustness and generalization. Extensive experiments over multiple downstream tasks demonstrate that UPET achieves a substantial improvement in terms of performance and efficiency. Our codes and data are released at https: //github.com/wjn1996/UPET.
TR-Rules: Rule-based Model for Link Forecasting on Temporal Knowledge Graph Considering Temporal Redundancy
Authors: Ningyuan Li; Haihong E; Shi Li; Mingzhi Sun; Tianyu Yao; Meina Song; Yong Wang; Haoran Luo
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Temporal knowledge graph (TKG) has been proved to be an effective way for modeling dynamic facts in real world. Many efforts have been devoted into predicting future events i.e. extrapolation, on TKGs. Recently, rule-based knowledge graph completion methods which are considered to be more interpretable than embedding-based methods, have been transferred to temporal knowledge graph extrapolation. However, rule-based models suffer from temporal redundancy when leveraged under dynamic settings, which results in inaccurate rule confidence calculation. In this paper, we define the problem of temporal redundancy and propose TR-Rules which solves the temporal redundancy issues through a simple but effective strategy. Besides, to capture more information lurking in TKGs, apart from cyclic rules, TR-Rules also mines and properly leverages acyclic rules, which has not been explored by existing models. Experimental results on three benchmarks show that TR-Rules achieves state-of-the-art performance. Ablation study shows the impact of temporal redundancy and demonstrates the performance of acyclic rules is much more promising due to its higher sensitivity to the number of sampled walks during learning stage.