GenAI4ELab/papercli-papers huggingface dataset:
2*n is better than n2: Decomposing Event Coreference Resolution into Two Tractable Problems
Authors: Shafiuddin Rehan Ahmed; Abhijnan Nath; James H. Martin; Nikhil Krishnaswamy
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Event Coreference Resolution (ECR) is the task of linking mentions of the same event either within or across documents. Most mention pairs are not coreferent, yet many that are coreferent can be identified through simple techniques such as lemma matching of the event triggers or the sentences in which they appear. Existing methods for training coreference systems sample from a largely skewed distribution, making it difficult for the algorithm to learn coreference beyond surface matching. Additionally, these methods are intractable because of the quadratic operations needed. To address these challenges, we break the problem of ECR into two parts: a) a heuristic to efficiently filter out a large number of non-coreferent pairs, and b) a training approach on a balanced set of coreferent and non-coreferent mention pairs. By following this approach, we show that we get comparable results to the state of the art on two popular ECR datasets while significantly reducing compute requirements. We also analyze the mention pairs that are “hard” to accurately classify as coreferent or non-coreferentcode repo: github.com/ahmeshaf/lemma_ce_coref.
SCCS: Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment
Authors: Jielin Qiu; Jiacheng Zhu; Mengdi Xu; Franck Dernoncourt; Trung Bui; Zhaowen Wang; Bo Li; Ding Zhao; Hailin Jin
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Multimedia summarization with multimodal output (MSMO) is a recently explored application in language grounding. It plays an essential role in real-world applications, i.e., automatically generating cover images and titles for news articles or providing introductions to online videos. However, existing methods extract features from the whole video and article and use fusion methods to select the representative one, thus usually ignoring the critical structure and varying semantics with video/document. In this work, we propose a Semantics-Consistent Cross-domain Summarization (SCCS) model based on optimal transport alignment with visual and textual segmentation. Our method first decomposes both videos and articles into segments in order to capture the structural semantics, and then follows a cross-domain alignment objective with optimal transport distance, which leverages multimodal interaction to match and select the visual and textual summary. We evaluated our method on three MSMO datasets, and achieved performance improvement by 8% & 6% of textual and 6.6% &5.7% of video summarization, respectively, which demonstrated the effectiveness of our method in producing high-quality multimodal summaries.
General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation
Authors: Rui Meng; Tong Wang; Xingdi Yuan; Yingbo Zhou; Daqing He
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Training keyphrase generation (KPG) models require a large amount of annotated data, which can be prohibitively expensive and often limited to specific domains. In this study, we first demonstrate that large distribution shifts among different domains severely hinder the transferability of KPG models. We then propose a three-stage pipeline, which gradually guides KPG models’ learning focus from general syntactical features to domain-related semantics, in a data-efficient manner. With domain-general phrase pre-training, we pre-train Sequence-to-Sequence models with generic phrase annotations that are widely available on the web, which enables the models to generate phrases in a wide range of domains. The resulting model is then applied in the Transfer Labeling stage to produce domain-specific pseudo keyphrases, which help adapt models to a new domain. Finally, we fine-tune the model with limited data with true labels to fully adapt it to the target domain. Our experiment results show that the proposed process can produce good quality keyphrases in new domains and achieve consistent improvements after adaptation with limited in-domain annotated data. All code and datasets are available at https://github.com/memray/OpenNMT-kpg-release.
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition
Authors: Zhen Zhang; Mengting Hu; Shiwan Zhao; Minlie Huang; Haotian Wang; Lemao Liu; Zhirui Zhang; Zhe Liu; Bingzhe Wu
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments. Evidential deep learning (EDL) has recently been proposed as a promising solution to explicitly model predictive uncertainty for classification tasks. However, directly applying EDL to NER applications faces two challenges, i.e., the problems of sparse entities and OOV/OOD entities in NER tasks. To address these challenges, we propose a trustworthy NER framework named E-NER by introducing two uncertainty-guided loss terms to the conventional EDL, along with a series of uncertainty-guided training strategies. Experiments show that E-NER can be applied to multiple NER paradigms to obtain accurate uncertainty estimation. Furthermore, compared to state-of-the-art baselines, the proposed method achieves a better OOV/OOD detection performance and better generalization ability on OOV entities.
LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented Language Model Prompting
Authors: Rita Ramos; Bruno Martins; Desmond Elliott
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Multilingual image captioning has recently been tackled by training with large-scale machine translated data, which is an expensive, noisy, and time-consuming process. Without requiring any multilingual caption data, we propose LMCap, an image-blind few-shot multilingual captioning model that works by prompting a language model with retrieved captions. Specifically, instead of following the standard encoder-decoder paradigm, given an image, LMCap first retrieves the captions of similar images using a multilingual CLIP encoder. These captions are then combined into a prompt for an XGLM decoder, in order to generate captions in the desired language. In other words, the generation model does not directly process the image, instead it processes retrieved captions. Experiments on the XM3600 dataset of geographically diverse images show that our model is competitive with fully-supervised multilingual captioning models, without requiring any supervised training on any captioning data.
Boosting Text Augmentation via Hybrid Instance Filtering Framework
Authors: Heng Yang; Ke Li
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Text augmentation is an effective technique for addressing the problem of insufficient data in natural language processing. However, existing text augmentation methods tend to focus on few-shot scenarios and usually perform poorly on large public datasets. Our research indicates that existing augmentation methods often generate instances with shifted feature spaces, which leads to a drop in performance on the augmented data (for example, EDA generally loses approximately 2% in aspect-based sentiment classification). To address this problem, we propose a hybrid instance-filtering framework (BoostAug) based on pre-trained language models that can maintain a similar feature space with natural datasets. BoostAug is transferable to existing text augmentation methods (such as synonym substitution and back translation) and significantly improves the augmentation performance by 2-3% in classification accuracy. Our experimental results on three classification tasks and nine public datasets show that BoostAug addresses the performance drop problem and outperforms state-of-the-art text augmentation methods. Additionally, we release the code to help improve existing augmentation methods on large datasets.
Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction
Authors: Thong Nguyen; Xiaobao Wu; Xinshuai Dong; Cong-Duy Nguyen; Zhen Hai; Lidong Bing; Anh Tuan Luu
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Multimodal Review Helpfulness Prediction (MRHP) aims to rank product reviews based on predicted helpfulness scores and has been widely applied in e-commerce via presenting customers with useful reviews. Previous studies commonly employ fully-connected neural networks (FCNNs) as the final score predictor and pairwise loss as the training objective. However, FCNNs have been shown to perform inefficient splitting for review features, making the model difficult to clearly differentiate helpful from unhelpful reviews. Furthermore, pairwise objective, which works on review pairs, may not completely capture the MRHP goal to produce the ranking for the entire review list, and possibly induces low generalization during testing. To address these issues, we propose a listwise attention network that clearly captures the MRHP ranking context and a listwise optimization objective that enhances model generalization. We further propose gradient-boosted decision tree as the score predictor to efficaciously partition product reviews’ representations. Extensive experiments demonstrate that our method achieves state-of-the-art results and polished generalization performance on two large-scale MRHP benchmark datasets.
Extract and Attend: Improving Entity Translation in Neural Machine Translation
Authors: Zixin Zeng; Rui Wang; Yichong Leng; Junliang Guo; Shufang Xie; Xu Tan; Tao Qin; Tie-Yan Liu
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: While Neural Machine Translation (NMT) has achieved great progress in recent years, it still suffers from inaccurate translation of entities (e.g., person/organization name, location), due to the lack of entity training instances. When we humans encounter an unknown entity during translation, we usually first look up in a dictionary and then organize the entity translation together with the translations of other parts to form a smooth target sentence. Inspired by this translation process, we propose an Extract-and-Attend approach to enhance entity translation in NMT, where the translation candidates of source entities are first extracted from a dictionary and then attended to by the NMT model to generate the target sentence. Specifically, the translation candidates are extracted by first detecting the entities in a source sentence and then translating the entities through looking up in a dictionary. Then, the extracted candidates are added as a prefix of the decoder input to be attended to by the decoder when generating the target sentence through self-attention. Experiments conducted on En-Zh and En-Ru demonstrate that the proposed method is effective on improving both the translation accuracy of entities and the overall translation quality, with up to 35% reduction on entity error rate and 0.85 gain on BLEU and 13.8 gain on COMET.
Real-World Compositional Generalization with Disentangled Sequence-to-Sequence Learning
Authors: Hao Zheng; Mirella Lapata
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Compositional generalization is a basic mechanism in human language learning, which current neural networks struggle with. A recently proposed Disentangled sequence-to-sequence model (Dangle) shows promising generalization capability by learning specialized encodings for each decoding step. We introduce two key modifications to this model which encourage more disentangled representations and improve its compute and memory efficiency, allowing us to tackle compositional generalization in a more realistic setting. Specifically, instead of adaptively re-encoding source keys and values at each time step, we disentangle their representations and only re-encode keys periodically, at some interval. Our new architecture leads to better generalization performance across existing tasks and datasets, and a new machine translation benchmark which we create by detecting naturally occurring compositional patterns in relation to a training set. We show this methodology better emulates real-world requirements than artificial challenges.
Cross-lingual AMR Aligner: Paying Attention to Cross-Attention
Authors: Abelardo Carlos Martínez Lorenzo; Pere Lluís Huguet Cabot; Roberto Navigli
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: This paper introduces a novel aligner for Abstract Meaning Representation (AMR) graphs that can scale cross-lingually, and is thus capable of aligning units and spans in sentences of different languages. Our approach leverages modern Transformer-based parsers, which inherently encode alignment information in their cross-attention weights, allowing us to extract this information during parsing. This eliminates the need for English-specific rules or the Expectation Maximization (EM) algorithm that have been used in previous approaches. In addition, we propose a guided supervised method using alignment to further enhance the performance of our aligner. We achieve state-of-the-art results in the benchmarks for AMR alignment and demonstrate our aligner’s ability to obtain them across multiple languages. Our code will be available at [https://www.github.com/babelscape/AMR-alignment](https://www.github.com/babelscape/AMR-alignment).
Zero-Shot Text Classification via Self-Supervised Tuning
Authors: Chaoqun Liu; Wenxuan Zhang; Guizhen Chen; Xiaobao Wu; Anh Tuan Luu; Chip Hong Chang; Lidong Bing
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Existing solutions to zero-shot text classification either conduct prompting with pre-trained language models, which is sensitive to the choices of templates, or rely on large-scale annotated data of relevant tasks for meta-tuning. In this work, we propose a new paradigm based on self-supervised learning to solve zero-shot text classification tasks by tuning the language models with unlabeled data, called self-supervised tuning. By exploring the inherent structure of free texts, we propose a new learning objective called first sentence prediction to bridge the gap between unlabeled data and text classification tasks. After tuning the model to learn to predict the first sentence in a paragraph based on the rest, the model is able to conduct zero-shot inference on unseen tasks such as topic classification and sentiment analysis. Experimental results show that our model outperforms the state-of-the-art baselines on 7 out of 10 tasks. Moreover, the analysis reveals that our model is less sensitive to the prompt design. Our code and pre-trained models are publicly available at https://github.com/DAMO-NLP-SG/SSTuning.
Logical Transformers: Infusing Logical Structures into Pre-Trained Language Models
Authors: Borui Wang; Qiuyuan Huang; Budhaditya Deb; Aaron Halfaker; Liqun Shao; Daniel McDuff; Ahmed Hassan Awadallah; Dragomir Radev; Jianfeng Gao
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Natural language contains rich logical structures and logical information, and correctly detecting and accurately understanding these logical structures and information underlying natural language texts is very crucial for NLP models’ performance on many important NLU and NLG tasks. Existing pre-trained language models based on the transformer architecture mostly adopt a classical design for constructing their input embeddings that ignores the logical structures underlying natural language texts, thus limiting their ability to better capture and encode key logical information in the input sequences. To overcome such limitations, in this paper we first propose a novel approach to construct logic-aware input embeddings for transformer language models through a combination of logic detection, logic mapping and hierarchical logical projections, and then develop a corresponding new modeling paradigm that can upgrade existing transformer language models into logical transformers to boost their performance on different NLU and NLG tasks. Our empirical experiments on four important and challenging NLU and NLG tasks demonstrate that our proposed logical transformer language models can achieve superior performance over their baseline transformer models through a deeper understanding of the logical structures of texts.
Large Language Models with Controllable Working Memory
Authors: Daliang Li; Ankit Singh Rawat; Manzil Zaheer; Xin Wang; Michal Lukasik; Andreas Veit; Felix Yu; Sanjiv Kumar
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP), partly owing to the massive amounts of world knowledge they memorize during pretraining. While many downstream applications provide the model with an informational context to aid its underlying task, how the model’s world knowledge interacts with the factual information presented in the context remains under explored. As a desirable behavior, an LLM should give precedence to the context whenever it contains task-relevant information that conflicts with the model’s memorized knowledge. This enables model predictions to be grounded in the context, which then facilitates updating specific model predictions without frequently retraining the model. By contrast, when the context is irrelevant to the task, the model should ignore it and fall back on its internal knowledge. In this paper, we undertake a first joint study of the aforementioned two properties, namely controllability and robustness, in the context of LLMs. We demonstrate that state-of-the-art T5 and PaLM models (both pretrained and finetuned) could exhibit low controllability and robustness that does not improve with increasing the model size. As a solution, we propose a simple yet effective method – knowledge aware finetuning (KAFT) – to strengthen both controllability and robustness by injecting counterfactual and irrelevant contexts to standard supervised datasets. Our comprehensive evaluation showcases the utility of KAFT across model architectures and sizes.
A Unified Evaluation Framework for Novelty Detection and Accommodation in NLP with an Instantiation in Authorship Attribution
Authors: Neeraj Varshney; Himanshu Gupta; Eric Robertson; Bing Liu; Chitta Baral
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: State-of-the-art natural language processing models have been shown to achieve remarkable performance in ‘closed-world’ settings where all the labels in the evaluation set are known at training time. However, in real-world settings, ‘novel’ instances that do not belong to any known class are often observed. This renders the ability to deal with novelties crucial. To initiate a systematic research in this important area of ‘dealing with novelties’, we introduce NoveltyTask, a multi-stage task to evaluate a system’s performance on pipelined novelty ‘detection’ and ‘accommodation’ tasks. We provide mathematical formulation of NoveltyTask and instantiate it with the authorship attribution task that pertains to identifying the correct author of a given text. We use amazon reviews corpus and compile a large dataset (consisting of 250k instances across 200 authors/labels) for NoveltyTask. We conduct comprehensive experiments and explore several baseline methods for the task. Our results show that the methods achieve considerably low performance making the task challenging and leaving sufficient room for improvement. Finally, we believe our work will encourage research in this underexplored area of dealing with novelties, an important step en route to developing robust systems.
CDA: A Contrastive Data Augmentation Method for Alzheimer’s Disease Detection
Authors: Junwen Duan; Fangyuan Wei; Jin Liu; Hongdong Li; Tianming Liu; Jianxin Wang
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Alzheimer’s Disease (AD) is a neurodegenerative disorder that significantly impacts a patient’s ability to communicate and organize language. Traditional methods for detecting AD, such as physical screening or neurological testing, can be challenging and time-consuming. Recent research has explored the use of deep learning techniques to distinguish AD patients from non-AD patients by analysing the spontaneous speech. These models, however, are limited by the availability of data. To address this, we propose a novel contrastive data augmentation method, which simulates the cognitive impairment of a patient by randomly deleting a proportion of text from the transcript to create negative samples. The corrupted samples are expected to be in worse conditions than the original by a margin. Experimental results on the benchmark ADReSS Challenge dataset demonstrate that our model achieves the best performance among language-based models.
Disentangling Aspect and Stance via a Siamese Autoencoder for Aspect Clustering of Vaccination Opinions
Authors: Lixing Zhu; Runcong Zhao; Gabriele Pergola; Yulan He
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Mining public opinions about vaccines from social media has been increasingly relevant to analyse trends in public debates and to provide quick insights to policy-makers. However, the application of existing models has been hindered by the wide variety of users’ attitudes and the new aspects continuously arising in the public debate. Existing approaches, frequently framed via well-known tasks, such as aspect classification or text span detection, make direct usage of the supervision information constraining the models to predefined aspect classes, while still not distinguishing those aspects from users’ stances. As a result, this has significantly hindered the dynamic integration of new aspects. We thus propose a model, namely Disentangled Opinion Clustering (DOC), for vaccination opinion mining from social media. DOC is able to disentangle users’ stances from opinions via a disentangling attention mechanism and a Swapping-Autoencoder, and is designed to process unseen aspect categories via a clustering approach, leveraging clustering-friendly representations induced by out-of-the-box Sentence-BERT encodings and disentangling mechanisms. We conduct a thorough experimental assessment demonstrating the benefit of the disentangling mechanisms and cluster-based approach on both the quality of aspect clusters and the generalization across new aspect categories, outperforming existing methodologies on aspect-based opinion mining.
Temporal Relation Classification using Boolean Question Answering
Authors: Omer Cohen; Kfir Bar
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Classifying temporal relations between a pair of events is crucial to natural language understanding and a well-known natural language processing task. Given a document and two event mentions, the task is aimed at finding which one started first. We propose an efficient approach for temporal relation classification (TRC) using a boolean question answering (QA) model which we fine-tune on questions that we carefully design based on the TRC annotation guidelines, thereby mimicking the way human annotators approach the task. Our new QA-based TRC model outperforms previous state-of-the-art results by 2.4%.