GenAI4ELab/papercli-papers huggingface dataset:
HiCL: Hierarchical Contrastive Learning of Unsupervised Sentence Embeddings
Authors: Zhuofeng Wu; Chaowei Xiao; VG Vinod Vydiswaran
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: In this paper, we propose a hierarchical contrastive learning framework, HiCL, which considers local segment-level and global sequence-level relationships to improve training efficiency and effectiveness. Traditional methods typically encode a sequence in its entirety for contrast with others, often neglecting local representation learning, leading to challenges in generalizing to shorter texts. Conversely, HiCL improves its effectiveness by dividing the sequence into several segments and employing both local and global contrastive learning to model segment-level and sequence-level relationships. Further, considering the quadratic time complexity of transformers over input tokens, HiCL boosts training efficiency by first encoding short segments and then aggregating them to obtain the sequence representation. Extensive experiments show that HiCL enhances the prior top-performing SNCSE model across seven extensively evaluated STS tasks, with an average increase of +0.2% observed on BERTlarge and +0.44% on RoBERTalarge.
Density-Aware Prototypical Network for Few-Shot Relation Classification
Authors: Jianfeng Wu; Mengting Hu; Yike Wu; Bingzhe Wu; Yalan Xie; Mingming Liu; Renhong Cheng
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: In recent years, few-shot relation classification has evoked many research interests. Yet a more challenging problem, i.e. none-of-the-above (NOTA), is under-explored. Existing works mainly regard NOTA as an extra class and treat it the same as known relations. However, such a solution ignores the overall instance distribution, where NOTA instances are actually outliers and distributed unnaturally compared with known ones. In this paper, we propose a density-aware prototypical network (D-Proto) to treat various instances distinctly. Specifically, we design unique training objectives to separate known instances and isolate NOTA instances, respectively. This produces an ideal instance distribution, where known instances are dense yet NOTAs have a small density. Moreover, we propose a NOTA detection module to further enlarge the density of known samples, and discriminate NOTA and known samples accurately. Experimental results demonstrate that the proposed method outperforms strong baselines with robustness towards various NOTA rates. The code will be made public after the paper is accepted.
Improved Training of Deep Text Clustering
Authors: Zonghao Yang; Wenpeng Hu; Yushan Tan; Zhunchen Luo
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: The classical deep clustering optimization methods basically leverage information such as clustering centers, mutual information, and distance metrics to construct implicit generalized labels to establish information feedback (weak supervision) and thus optimize the deep model. However, the resulting generalized labels have different degrees of errors in the whole clustering process due to the limitation of clustering accuracy, which greatly interferes with the clustering process. To this end, this paper proposes a general deep clustering optimization method from the perspective of empirical risk minimization, using the correlation relationship between the samples. Experiments on two classical deep clustering methods demonstrate the necessity and effectiveness of the method. Code is available at https://github.com/yangzonghao1024/DCGLU.
RegaVAE: A Retrieval-Augmented Gaussian Mixture Variational Auto-Encoder for Language Modeling
Authors: Jingcheng Deng; Liang Pang; Huawei Shen; Xueqi Cheng
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Retrieval-augmented language models show promise in addressing issues like outdated information and hallucinations in language models (LMs). However, current research faces two main problems: 1) determining what information to retrieve, and 2) effectively combining retrieved information during generation. We argue that valuable retrieved information should not only be related to the current source text but also consider the future target text, given the nature of LMs that model future tokens. Moreover, we propose that aggregation using latent variables derived from a compact latent space is more efficient than utilizing explicit raw text, which is limited by context length and susceptible to noise. Therefore, we introduce RegaVAE, a retrieval-augmented language model built upon the variational auto-encoder (VAE). It encodes the text corpus into a latent space, capturing current and future information from both source and target text. Additionally, we leverage the VAE to initialize the latent space and adopt the probabilistic form of the retrieval generation paradigm by expanding the Gaussian prior distribution into a Gaussian mixture distribution. Theoretical analysis provides an optimizable upper bound for RegaVAE. Experimental results on various datasets demonstrate significant improvements in text generation quality and hallucination removal.
RefGPT: Dialogue Generation of GPT, by GPT, and for GPT
Authors: Dongjie Yang; Ruifeng Yuan; Yuantao Fan; Yifei Yang; Zili Wang; Shusen Wang; Hai Zhao
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Large Language Models (LLMs) have attained the impressive capability to resolve a wide range of NLP tasks by fine-tuning high-quality instruction data. However, collecting human-written data of high quality, especially multi-turn dialogues, is expensive and unattainable for most people. Though previous studies have used powerful LLMs to generate the dialogues automatically, they all suffer from generating untruthful dialogues because of the model hallucination. Therefore, we propose a method called RefGPT to generate enormous truthful and customized dialogues without worrying about factual errors caused by the model hallucination. RefGPT solves the model hallucination in dialogue generation by restricting the LLMs to leverage the given reference instead of reciting their own knowledge to generate dialogues. Additionally, RefGPT adds detailed controls on every utterance to enable high customization capability, which previous studies have ignored. On the basis of RefGPT, we also propose two high-quality dialogue datasets generated by GPT-4, namely **RefGPT-Fact** and **RefGPT-Code**. RefGPT-Fact is a dataset with 100k multi-turn dialogues based on factual knowledge and RefGPT-Code has 76k multi-turn dialogues covering a wide range of coding scenarios. Our code and datasets are released in https://github.com/mutonix/RefGPT.
INA: An Integrative Approach for Enhancing Negotiation Strategies with Reward-Based Dialogue Agent
Authors: Zishan Ahmad; Suman Saurabh; Vaishakh Menon; Asif Ekbal; Roshni Ramnani; Anutosh Maitra
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: In this paper, we propose a novel negotiation agent designed for the online marketplace. Our dialogue agent is integrative in nature i.e, it possesses the capability to negotiate on price as well as other factors, such as the addition or removal of items from a deal bundle, thereby offering a more flexible and comprehensive negotiation experience. To enable this functionality, we create a new dataset called Integrative Negotiation Dataset (IND). For this dataset creation, we introduce a new semi-automated data creation method, which combines defining negotiation intents, actions, and intent-action simulation between users and the agent to generate potential dialogue flows. Finally, the prompting of GPT-J, a state-of-the-art language model, is done to generate dialogues for a given intent, with a human-in-the-loop process for post-editing and refining minor errors to ensure high data quality. We first train a maximum likelihood loss based model on IND, and then employ a set of novel rewards specifically tailored for the negotiation task to train our Integrative Negotiation Agent (INA). These rewards incentivize the agent to learn effective negotiation strategies that can adapt to various contextual requirements and price proposals. We train our model and conduct experiments to evaluate the effectiveness of our reward-based dialogue agent for negotiation. Our results demonstrate that the proposed approach and reward functions significantly enhance the negotiation capabilities of the dialogue agent. The INA successfully engages in integrative negotiations, displaying the ability to dynamically adjust prices and negotiate the inclusion or exclusion of items in a deal bundle.
Large Language Models are Better Reasoners with Self-Verification
Authors: Yixuan Weng; Minjun Zhu; Fei Xia; Bin Li; Shizhu He; Shengping Liu; Bin Sun; Kang Liu; Jun Zhao
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Recently, with the chain of thought (CoT) prompting, large language models (LLMs), e.g., GPT-3, have shown strong reasoning ability in several natural language processing tasks such as arithmetic, commonsense, and logical reasoning. However, LLMs with CoT require multi-step prompting and multi-token prediction, which is highly sensitive to individual mistakes and vulnerable to error accumulation. The above issues make the LLMs need the ability to verify the answers. In fact, after inferring conclusions in some thinking decision tasks, people often check them by re-verifying steps to avoid some mistakes. In this paper, we propose and prove that LLMs also have similar self-verification abilities. We take the conclusion obtained by CoT as one of the conditions for solving the original problem. By performing a backward verification of the answers that LLM deduced for itself, we can obtain interpretable answer validation scores to select the candidate answer with the highest score. Experimental results demonstrate that the proposed method can improve the reasoning performance on various arithmetic, commonsense, and logical reasoning datasets. Our code is publicly available at: https://github.com/WENGSYX/Self-Verification.
Multi-Granularity Information Interaction Framework for Incomplete Utterance Rewriting
Authors: Haowei Du; Dinghao Zhang; Chen Li; Yang Li; Dongyan Zhao
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Recent approaches in Incomplete Utterance Rewriting (IUR) fail to capture the source of important words, which is crucial to edit the incomplete utterance, and introduce words from irrelevant utterances. We propose a novel and effective multi-task information interaction framework including context selection, edit matrix construction, and relevance merging to capture the multi-granularity of semantic information. Benefiting from fetching the relevant utterance and figuring out the important words, our approach outperforms existing state-of-the-art models on two benchmark datasets Restoration-200K and CANAND in this field.
Accuracy is not enough: Evaluating Personalization in Summarizers
Authors: Rahul Vansh; Darsh Rank; Sourish Dasgupta; Tanmoy Chakraborty
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Text summarization models are evaluated in terms of their accuracy and quality using various measures such as ROUGE, BLEU, METEOR, BERTScore, PYRAMID, readability, and several other recently proposed ones. The central objective of all accuracy measures is to evaluate the model’s ability to capture saliency accurately. Since saliency is subjective w.r.t the readers’ preferences, there cannot be a fit-all summary for a given document. This means that in many use-cases, summarization models need to be personalized w.r.t user-profiles. However, to our knowledge, there is no measure to evaluate the degree-of-personalization of a summarization model. In this paper, we first establish that existing accuracy measures cannot evaluate the degree of personalization of any summarization model, and then propose a novel measure, called EGISES, for automatically computing the same. Using the PENS dataset released by Microsoft Research, we analyze the degree of personalization of ten different state-of-the-art summarization models (both extractive and abstractive), five of which are explicitly trained for personalized summarization, and the remaining are appropriated to exhibit personalization. We conclude by proposing a generalized accuracy measure, called P-Accuracy, for designing accuracy measures that should also take personalization into account and demonstrate the robustness and reliability of the measure through meta-evaluation.
For Generated Text, Is NLI-Neutral Text the Best Text?
Authors: Michail Mersinias; Kyle Mahowald
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: We explore incorporating natural language inference (NLI) into the text generative pipeline by using a pre-trained NLI model to assess whether a generated sentence entails, contradicts, or is neutral to the prompt and preceding text. First, we show that the NLI task is predictive of generation errors made by GPT-3. We use these results to develop an NLI-informed generation procedure for GPT-J. Then, we evaluate these generations by obtaining human annotations on error types and overall quality. We find that an NLI strategy of maximizing entailment improves text generation when the nucleus sampling randomness parameter value is high, while one which maximizes contradiction is in fact productive when the parameter value is low. Overall, though, we demonstrate that an NLI strategy of maximizing the neutral class provides the highest quality of generated text (significantly better than the vanilla generations), regardless of parameter value.
Combining Counting Processes and Classification Improves a Stopping Rule for Technology Assisted Review
Authors: Reem Bin-Hezam; Mark Stevenson
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Technology Assisted Review (TAR) stopping rules aim to reduce the cost of manually assessing documents for relevance by minimising the number of documents that need to be examined to ensure a desired level of recall. This paper extends an effective stopping rule using information derived from a text classifier that can be trained without the need for any additional annotation. Experiments on multiple data sets (CLEF e-Health, TREC Total Recall, TREC Legal and RCV1) showed that the proposed approach consistently improves performance and outperforms several alternative methods.
Complexity-Guided Curriculum Learning for Text Graphs
Authors: Nidhi Vakil; Hadi Amiri
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Curriculum learning provides a systematic approach to training. It refines training progressively, tailors training to task requirements, and improves generalization through exposure to diverse examples. We present a curriculum learning approach that builds on existing knowledge about text and graph complexity formalisms for training with text graph data. The core part of our approach is a novel data scheduler, which employs “spaced repetition” and complexity formalisms to guide the training process. We demonstrate the effectiveness of the proposed approach on several text graph tasks and graph neural network architectures. The proposed model gains more and uses less data; consistently prefers text over graph complexity indices throughout training, while the best curricula derived from text and graph complexity indices are equally effective; and it learns transferable curricula across GNN models and datasets. In addition, we find that both node-level (local) and graph-level (global) graph complexity indices, as well as shallow and traditional text complexity indices play a crucial role in effective curriculum learning.
CoVariance-based Causal Debiasing for Entity and Relation Extraction
Authors: Lin Ren; Yongbin Liu; Yixin Cao; Chunping Ouyang
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Joint entity and relation extraction tasks aim to recognize named entities and extract relations simultaneously. Suffering from a variety of data biases, such as data selection bias, and distribution bias (out of distribution, long-tail distribution), serious concerns can be witnessed to threaten the model’s transferability, robustness, and generalization. In this work, we address the above problems from a causality perspective. We propose a novel causal framework called c ̲ovariance and  ̲variance  ̲optimization framework (OVO) to optimize feature representations and conduct general debiasing. In particular, the proposed  ̲covariance  ̲optimizing (COP) minimizes characterizing features’ covariance for alleviating the selection and distribution bias and enhances feature representation in the feature space. Furthermore, based on the causal backdoor adjustment, we propose \\underlinevariance  ̲optimizing (VOP) separates samples in terms of label information and minimizes the variance of each dimension in the feature vectors of the same class label for mitigating the distribution bias further. By applying it to three strong baselines in two widely used datasets, the results demonstrate the effectiveness and generalization of OVO for joint entity and relation extraction tasks. Furthermore, a fine-grained analysis reveals that OVO possesses the capability to mitigate the impact of long-tail distribution.
Multi-label and Multi-target Sampling of Machine Annotation for Computational Stance Detection
Authors: Zhengyuan Liu; Hai Leong Chieu; Nancy Chen
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Data collection from manual labeling provides domain-specific and task-aligned supervision for data-driven approaches, and a critical mass of well-annotated resources is required to achieve reasonable performance in natural language processing tasks. However, manual annotations are often challenging to scale up in terms of time and budget, especially when domain knowledge, capturing subtle semantic features, and reasoning steps are needed. In this paper, we investigate the efficacy of leveraging large language models on automated labeling for computational stance detection. We empirically observe that while large language models show strong potential as an alternative to human annotators, their sensitivity to task-specific instructions and their intrinsic biases pose intriguing yet unique challenges in machine annotation. We introduce a multi-label and multi-target sampling strategy to optimize the annotation quality. Experimental results on the benchmark stance detection corpora show that our method can significantly improve performance and learning efficacy.
In What Languages are Generative Language Models the Most Formal? Analyzing Formality Distribution across Languages
Authors: Asım Ersoy; Gerson Vizcarra; Tahsin Mayeesha; Benjamin Muller
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Multilingual generative language models (LMs) are increasingly fluent in a large variety of languages. Trained on the concatenation of corpora in multiple languages, they enable powerful transfer from high-resource languages to low-resource ones. However, it is still unknown what cultural biases are induced in the predictions of these models. In this work, we focus on one language property highly influenced by culture: formality. We analyze the formality distributions of XGLM and BLOOM’s predictions, two popular generative multilingual language models, in 5 languages. We classify 1,200 generations per language as formal, informal, or incohesive and measure the impact of the prompt formality on the predictions. Overall, we observe a diversity of behaviors across the models and languages. For instance, XGLM generates informal text in Arabic and Bengali when conditioned with informal prompts, much more than BLOOM. In addition, even though both models are highly biased toward the formal style when prompted neutrally, we find that the models generate a significant amount of informal predictions even when prompted with formal text. We release with this work 6,000 annotated samples, paving the way for future work on the formality of generative multilingual LMs.
MaXM: Towards Multilingual Visual Question Answering
Authors: Soravit Changpinyo; Linting Xue; Michal Yarom; Ashish Thapliyal; Idan Szpektor; Julien Amelot; Xi Chen; Radu Soricut
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Visual Question Answering (VQA) has been primarily studied through the lens of the English language. Yet, tackling VQA in other languages in the same manner would require a considerable amount of resources. In this paper, we propose scalable solutions to multilingual visual question answering (mVQA), on both data and modeling fronts. We first propose a translation-based framework to mVQA data generation that requires much less human annotation efforts than the conventional approach of directly collection questions and answers. Then, we apply our framework to the multilingual captions in the Crossmodal-3600 dataset and develop an efficient annotation protocol to create MaXM, a test-only VQA benchmark in 7 diverse languages. Finally, we develop a simple, lightweight, and effective approach as well as benchmark state-of-the-art English and multilingual VQA models. We hope that our benchmark encourages further research on mVQA.
Efficient Latent Variable Modeling for Knowledge-Grounded Dialogue Generation
Authors: Gunsoo Han; Daejin Jo; Daniel Nam; Eunseop Yoon; Taehwan Kwon; Seungeun Rho; Kyoung-Woon On; Chang Yoo; Sungwoong Kim
Venue: EMNLP 2023 (Findings of the Association for Computational Linguistics: EMNLP 2023)

Abstract: Knowledge-grounded dialogue generation requires first retrieving appropriate external knowledge based on a conversational context and then generating a response grounded on the retrieved knowledge. In general, these two sequential modules, a knowledge retriever and a response generator, have been separately trained in a supervised manner. However, obtaining intermediate labels of the ground-truth knowledge is expensive, especially in open-domain conversations. Latent variable modeling avoids this need for the labels. In this paper, we propose an efficient algorithm for this latent variable modeling that is able to leverage a large amount of dialogue data. Rather than directly training the complex retriever, we adapt a query generator with an off-the-shelf retriever, and the query generator and response generator are simultaneously trained over the latent variable of query. Moreover, we employ lower bound of the evidence as a training objective and modify it to robustly perform the joint training. Experimental results on diverse knowledge-grounded dialogue datasets show that the proposed algorithm significantly outperforms the supervised learning algorithm even without the use of the annotated knowledge while maintaining efficiency and scalability.