GenAI4ELab/papercli-papers huggingface dataset:
RISE: Leveraging Retrieval Techniques for Summarization Evaluation
Authors: David Uthus; Jianmo Ni
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Evaluating automatically-generated text summaries is a challenging task. While there have been many interesting approaches, they still fall short of human evaluations. We present RISE, a new approach for evaluating summaries by leveraging techniques from information retrieval. RISE is first trained as a retrieval task using a dual-encoder retrieval setup, and can then be subsequently utilized for evaluating a generated summary given an input document, without gold reference summaries. RISE is especially well suited when working on new datasets where one may not have reference summaries available for evaluation. We conduct comprehensive experiments on the SummEval benchmark (Fabbri et al., 2021) and a long document summarization benchmark. The results show that RISE consistently achieves higher correlation with human evaluations compared to many past approaches to summarization evaluation. Furthermore, RISE also demonstrates data-efficiency and generalizability across languages.
On the Difference of BERT-style and CLIP-style Text Encoders
Authors: Zhihong Chen; Guiming Chen; Shizhe Diao; Xiang Wan; Benyou Wang
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Masked language modeling (MLM) has been one of the most popular pretraining recipes in natural language processing, e.g., BERT, one of the representative models. Recently, contrastive language-image pretraining (CLIP) has also attracted attention, especially its vision models that achieve excellent performance on a broad range of vision tasks. However, few studies are dedicated to studying the text encoders learned by CLIP. In this paper, we analyze the difference between BERT-style and CLIP-style text encoders from three experiments: (i) general text understanding, (ii) vision-centric text understanding, and (iii) text-to-image generation. Experimental analyses show that although CLIP-style text encoders underperform BERT-style ones for general text understanding tasks, they are equipped with a unique ability, i.e., synesthesia, for the cross-modal association, which is more similar to the senses of humans.
Model Interpretability and Rationale Extraction by Input Mask Optimization
Authors: Marc Brinner; Sina Zarrieß
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Concurrent with the rapid progress in neural network-based models in NLP, the need for creating explanations for the predictions of these black-box models has risen steadily. Yet, especially for complex inputs like texts or images, existing interpretability methods still struggle with deriving easily interpretable explanations that also accurately represent the basis for the model’s decision. To this end, we propose a new, model-agnostic method to generate extractive explanations for predictions made by neural networks, that is based on masking parts of the input which the model does not consider to be indicative of the respective class. The masking is done using gradient-based optimization combined with a new regularization scheme that enforces sufficiency, comprehensiveness, and compactness of the generated explanation. Our method achieves state-of-the-art results in a challenging paragraph-level rationale extraction task, showing that this task can be performed without training a specialized model. We further apply our method to image inputs and obtain high-quality explanations for image classifications, which indicates that the objectives for optimizing explanation masks in text generalize to inputs of other modalities.
NusaCrowd: Open Source Initiative for Indonesian NLP Resources
Authors: Samuel Cahyawijaya; Holy Lovenia; Alham Fikri Aji; Genta Winata; Bryan Wilie; Fajri Koto; Rahmad Mahendra; Christian Wibisono; Ade Romadhony; Karissa Vincentio; Jennifer Santoso; David Moeljadi; Cahya Wirawan; Frederikus Hudi; Muhammad Satrio Wicaksono; Ivan Parmonangan; Ika Alfina; Ilham Firdausi Putra; Samsul Rahmadani; Yulianti Oenang; Ali Septiandri; James Jaya; Kaustubh Dhole; Arie Suryani; Rifki Afina Putri; Dan Su; Keith Stevens; Made Nindyatama Nityasya; Muhammad Adilazuarda; Ryan Hadiwijaya; Ryandito Diandaru; Tiezheng Yu; Vito Ghifari; Wenliang Dai; Yan Xu; Dyah Damapuspita; Haryo Wibowo; Cuk Tho; Ichwanul Karo Karo; Tirana Fatyanosa; Ziwei Ji; Graham Neubig; Timothy Baldwin; Sebastian Ruder; Pascale Fung; Herry Sujaini; Sakriani Sakti; Ayu Purwarianti
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments.NusaCrowd’s data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia. Our work strives to advance natural language processing (NLP) research for languages that are under-represented despite being widely spoken.
Transcribing Vocal Communications of Domestic Shiba lnu Dogs
Authors: Jieyi Huang; Chunhao Zhang; Mengyue Wu; Kenny Zhu
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: How animals communicate and whether they have languages is a persistent curiosity of human beings. However, the study of animal communications has been largely restricted to data from field recordings or in a controlled environment, which is expensive and limited in scale and variety. In this paper, we take domestic Shiba Inu dogs as an example, and extract their vocal communications from large amount of YouTube videos of Shiba Inu dogs. We classify these clips into different scenarios and locations, and further transcribe the audio into phonetically symbolic scripts through a systematic process. We discover consistent phonetic symbols among their expressions, which indicates that Shiba Inu dogs can have systematic verbal communication patterns. This reusable framework produces the first-of-its-kind Shiba Inu vocal communication dataset that will be valuable to future research in both zoology and linguistics.
SkillQG: Learning to Generate Question for Reading Comprehension Assessment
Authors: Xiaoqiang Wang; Bang Liu; Siliang Tang; Lingfei Wu
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: We present SkillQG: a question generation framework with controllable comprehension types for assessing and improving machine reading comprehension models. Existing question generation systems widely differentiate questions by literal information such as question words and answer types to generate semantically relevant questions for a given context. However, they rarely consider the comprehension nature of questions, i.e., the different comprehension capabilities embodied by different questions. In comparison, our SkillQG is able to tailor a fine-grained assessment and improvement to the capabilities of questions answering models built on it. Specifically, we first frame the comprehension type of questions based on a hierarchical skill-based schema. We then formulate SkillQG as a skill-conditioned question generator. Furthermore, to improve the controllability of generation, we augment the input text with skill-specific question focus and knowledge, which are constructed by iteratively prompting the pre-trained language models. Empirical results demonstrate that SkillQG outperforms baselines in terms of quality, relevance, and skill-controllability while showing a promising performance boost in downstream question answering task.
Improving Long Dialogue Summarization with Semantic Graph Representation
Authors: Yilun Hua; Zhaoyuan Deng; Kathleen McKeown
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Although Large Language Models (LLMs) are successful in abstractive summarization of short dialogues, summarization of long dialogues remains challenging. To address this challenge, we propose a novel algorithm that processes complete dialogues comprising thousands of tokens into topic-segment-level Abstract Meaning Representation (AMR) graphs, which explicitly capture the dialogue structure, highlight salient semantics, and preserve high-level information. We also develop a new text-graph attention to leverage both graph semantics and a pretrained LLM that exploits the text. Finally, we propose an AMR node selection loss used jointly with conventional cross-entropy loss, to create additional training signals that facilitate graph feature encoding and content selection. Experiments show that our system outperforms the state-of-the-art models on multiple long dialogue summarization datasets, especially in low-resource settings, and generalizes well to out-of-domain data.
Model Intrinsic Features of Fine-tuning based Text Summarization Models for Factual Consistency
Authors: Jongyoon Song; Nohil Park; Bongkyu Hwang; Jaewoong Yun; Seongho Joe; Youngjune Gwon; Sungroh Yoon
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: In this study, we analyze the model intrinsic features of a summarization model by varying the fine-tuning objectives and datasets. We fine-tune BART models combining three fine-tuning objectives (negative log-likelihood, unlikelihood, and contrastive loss) and two datasets (CNN/DailyMail and XSum) and provide shuffled or aligned documents to observe changes in the model predictions and intrinsic features. We find that (i) the inductive bias for factual consistency during the fine-tuning procedure depends on both the objectives and datasets, and (ii) summarization models with relatively low factual consistency are more likely to model summaries that are not conditional to the documents. We demonstrate that splitting data based on the unconditional and conditional summary modeling difficulty affects the factual consistency and intrinsic features of the summarization models. Our experimental results highlight the importance of studying the inductive bias during fine-tuning for factual consistency.
EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive Pruning
Authors: Tiannan Wang; Wangchunshu Zhou; Yan Zeng; Xinsong Zhang
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Pre-trained vision-language models (VLMs) have achieved impressive results in a range of vision-language tasks. However, popular VLMs usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and deployment in real-world applications due to space, memory, and latency constraints. In this work, we introduce a distilling then pruning framework to compress large vision-language models into smaller, faster, and more accurate ones. We first shrink the size ofa pre-trained large VLM and apply knowledge distillation in the vision-language pre-training stage to obtain a task-agnostic compact VLM. Then we propose a modal-adaptive pruning algorithm to automatically infer the importance of vision and language modalities for different downstream tasks and adaptively remove redundant structures and neurons in different encoders with controllable target sparsity. We apply our framework to train EfficientVLM, a fast and accurate vision-language model consisting of 6 vision layers, 3 text layers, and 3 cross-modal fusion layers, accounting for only 93 million parameters in total, which is 44.3% of the teacher model. EfficientVLM retains 98.4% performance of the teacher model and accelerates its inference speed by 2.2×. EfficientVLM achieves a large absolute improvement over previous SoTA efficient VLMs of similar sizes by a large margin on various vision-language tasks, including VQAv2 (+4.9%), NLVR2 (+5.6%), ITR (R@1 on TR +17.2%, on IR + 15.6% ) and COCO caption generation (CIDEr +6.5), demonstrating a large potential on training lightweight VLMs.
DP-BART for Privatized Text Rewriting under Local Differential Privacy
Authors: Timour Igamberdiev; Ivan Habernal
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Privatized text rewriting with local differential privacy (LDP) is a recent approach that enables sharing of sensitive textual documents while formally guaranteeing privacy protection to individuals. However, existing systems face several issues, such as formal mathematical flaws, unrealistic privacy guarantees, privatization of only individual words, as well as a lack of transparency and reproducibility. In this paper, we propose a new system ‘DP-BART’ that largely outperforms existing LDP systems. Our approach uses a novel clipping method, iterative pruning, and further training of internal representations which drastically reduces the amount of noise required for DP guarantees. We run experiments on five textual datasets of varying sizes, rewriting them at different privacy guarantees and evaluating the rewritten texts on downstream text classification tasks. Finally, we thoroughly discuss the privatized text rewriting approach and its limitations, including the problem of the strict text adjacency constraint in the LDP paradigm that leads to the high noise requirement.
Robustness of Learning from Task Instructions
Authors: Jiasheng Gu; Hongyu Zhao; Hanzi Xu; Liangyu Nie; Hongyuan Mei; Wenpeng Yin
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Traditional supervised learning mostly works on individual tasks and requires training on a large set of task-specific examples. This paradigm seriously hinders the development of task generalization since preparing a task-specific example set is costly. To build a system that can quickly and easily generalize to new tasks, task instructions have been adopted as an emerging trend of supervision recently. These instructions give the model the definition of the task and allow the model to output the appropriate answer based on the instructions and inputs. However, task instructions are often expressed in different forms, which can be interpreted from two threads: first, some instructions are short sentences and are pretrained language model (PLM) oriented, such as prompts, while other instructions are paragraphs and are human-oriented, such as those in Amazon MTurk; second, different end-users very likely explain the same task with instructions of different textual expressions. A robust system for task generalization should be able to handle any new tasks regardless of the variability of instructions. However, the system robustness in dealing with instruction-driven task generalization is still unexplored. This work investigates the system robustness when the instructions of new tasks are (i) manipulated, (ii) paraphrased, or (iii) from different levels of conciseness. To our knowledge, this is the first work that systematically studies how robust a PLM is when it is supervised by instructions with different factors of variability.
Masked Latent Semantic Modeling: an Efficient Pre-training Alternative to Masked Language Modeling
Authors: Gábor Berend
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: In this paper, we propose an alternative to the classic masked language modeling (MLM) pre-training paradigm, where the objective is altered from the reconstruction of the exact identity of randomly selected masked subwords to the prediction of their latent semantic properties. We coin the proposed pre-training technique masked latent semantic modeling (MLSM for short). In order to make the contextualized determination of the latent semantic properties of the masked subwords possible, we rely on an unsupervised technique which uses sparse coding. Our experimental results reveal that the fine-tuned performance of those models that we pre-trained via MLSM is consistently and significantly better compared to the use of vanilla MLM pretraining and other strong baselines.
Detection and Mitigation of the Negative Impact of Dataset Extractivity on Abstractive Summarization
Authors: Yubin Ge; Sullam Jeoung; Ly Dinh; Jana Diesner
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: In text summarization, extractivity is defined as a measurement of the degree of overlap between a source document and its summary. Previous research has shown that the extractivity level of training data can influence both output extractivity and the amount of factual information (i.e. faithfulness) in outputs for abstractive summarization. However, it remains unclear if and how extractivity impacts the performance of abstractive models. In this work, we investigate the relationship between dataset extractivity and model performance by comparing the performance of trained models under different degrees of extractivity. We find that while low levels of extractivity can improve performance, as extractivity increases, performance is negatively impacted. Furthermore, through an analysis of the model’s copy continuity of content, we discover that higher extractivity leads to a greater tendency for the model to copy text continuously from the source document rather than identifying and summarizing important content that should be covered in the target summary. To address these issues, we propose a simple and effective method to design copy labels for fixing the model’s copying behaviors and train the model with a copy mechanism. The experimental results illustrate the effectiveness of our strategy in alleviating the negative impact on model performance resulting from high dataset extractivity, and that our method outperforms several competitive baselines.
Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node Clustering
Authors: Siwei Wu; Xiangqing Shen; Rui Xia
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: The nodes in the commonsense knowledge graph (CSKG) are normally represented by free-form short text (e.g., word or phrase). Different nodes may represent the same concept. This leads to the problems of edge sparsity and node redundancy, which challenges CSKG representation and completion. On the one hand, edge sparsity limits the performance of graph representation learning; On the other hand, node redundancy makes different nodes corresponding to the same concept have inconsistent relations with other nodes. To address the two problems, we propose a new CSKG completion framework based on Contrastive Pretraining and Node Clustering (CPNC). Contrastive Pretraining constructs positive and negative head-tail node pairs on CSKG and utilizes contrastive learning to obtain better semantic node representation. Node Clustering aggregates nodes with the same concept into a latent concept, assisting the task of CSKG completion. We evaluate our CPNC approach on two CSKG completion benchmarks (CN-100K and ATOMIC), where CPNC outperforms the state-of-the-art methods. Extensive experiments demonstrate that both Contrastive Pretraining and Node Clustering can significantly improve the performance of CSKG completion. The source code of CPNC is publicly available on https://github.com/NUSTM/CPNC.
Incorporating Factuality Inference to Identify Document-level Event Factuality
Authors: Heng Zhang; Peifeng Li; Zhong Qian; Xiaoxu Zhu
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Document-level Event Factuality Identification (DEFI) refers to identifying the degree of certainty that a specific event occurs in a document. Previous studies on DEFI failed to link the document-level event factuality with various sentence-level factuality values in the same document. In this paper, we innovatively propose an event factuality inference task to bridge the sentence-level and the document-level event factuality semantically. Specifically, we present a Sentence-to-Document Inference Network (SDIN) that contains a multi-layer interaction module and a gated aggregation module to integrate the above two tasks, and employ a multi-task learning framework to improve the performance of DEFI. The experimental results on the public English and Chinese DLEF datasets show that our model outperforms the SOTA baselines significantly.
Hybrid and Collaborative Passage Reranking
Authors: Zongmeng Zhang; Wengang Zhou; Jiaxin Shi; Houqiang Li
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: In passage retrieval system, the initial passage retrieval results may be unsatisfactory, which can be refined by a reranking scheme. Existing solutions to passage reranking focus on enriching the interaction between query and each passage separately, neglecting the context among the top-ranked passages in the initial retrieval list. To tackle this problem, we propose a Hybrid and Collaborative Passage Reranking (HybRank) method, which leverages the substantial similarity measurements of upstream retrievers for passage collaboration and incorporates the lexical and semantic properties of sparse and dense retrievers for reranking. Besides, built on off-the-shelf retriever features, HybRank is a plug-in reranker capable of enhancing arbitrary passage lists including previously reranked ones. Extensive experiments demonstrate the stable improvements of performance over prevalent retrieval and reranking methods, and verify the effectiveness of the core components of HybRank.
Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence
Authors: Haoran Li; Mingshi Xu; Yangqiu Song
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Sentence-level representations are beneficial for various natural language processing tasks. It is commonly believed that vector representations can capture rich linguistic properties. Currently, large language models (LMs) achieve state-of-the-art performance on sentence embedding. However, some recent works suggest that vector representations from LMs can cause information leakage. In this work, we further investigate the information leakage issue and propose a generative embedding inversion attack (GEIA) that aims to reconstruct input sequences based only on their sentence embeddings. Given the black-box access to a language model, we treat sentence embeddings as initial tokens’ representations and train or fine-tune a powerful decoder model to decode the whole sequences directly. We conduct extensive experiments to demonstrate that our generative inversion attack outperforms previous embedding inversion attacks in classification metrics and generates coherent and contextually similar sentences as the original inputs.