mtybilly/Instruct2DS huggingface dataset:
Identifying Human Strategies for Generating Word-Level Adversarial Examples
Authors: Bennett Kleinberg, Lewis Griffin Maximilian Mozes
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Adversarial examples in NLP are receiving increasing research attention. One line of investigation is the generation of word-level adversarial examples against fine-tuned Transformer models that preserve naturalness and grammaticality. Previous work found that human- and machine-generated adversarial examples are comparable in their naturalness and grammatical correctness. Most notably, humans were able to generate adversarial examples much more effortlessly than automated attacks. In this paper, we provide a detailed analysis of exactly how humans create these adversarial examples. By exploring the behavioural patterns of human workers during the generation process, we identify statistically significant tendencies based on which words humans prefer to select for adversarial replacement (e.g., word frequencies, word saliencies, sentiment) as well as where and when words are replaced in an input sequence. With our findings, we seek to inspire efforts that harness human strategies for more robust NLP models.
Refinement Matters: Textual Description Needs to be Refined for Zero-shot Learning
Authors: Sethupathy Parameswaran, Vinay Verma, Suresh Sundaram, Savitha Ramasamy Chandan Gautam
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Zero-Shot Learning (ZSL) has shown great promise at the intersection of vision and language, and generative methods for ZSL are predominant owing to their efficiency. Moreover, textual description or attribute plays a critical role in transferring knowledge from the seen to unseen classes in ZSL. Such generative approaches for ZSL are very costly to train and require the class description of the unseen classes during training. In this work, we propose a non-generative gating-based attribute refinement network for ZSL, which achieves similar accuracies to generative methods of ZSL, at a much lower computational cost. The refined attributes are mapped into the visual domain through an attribute embedder, and the whole network is guided by the circle loss and the well-known softmax cross-entropy loss to obtain a robust class embedding. We refer to our approach as Circle loss guided gating-based Attribute-Refinement Network (CARNet). We perform extensive experiments on the five benchmark datasets over the various challenging scenarios viz., Generalized ZSL (GZSL), Continual GZSL (CGZSL), and conventional ZSL. We observe that the CARNet significantly outperforms recent non-generative ZSL methods and most generative ZSL methods in all three settings by a significant margin. Our extensive ablation study disentangles the performance of various components and justifies their importance. The source code is available at https://github.com/Sethup123/CARNet.
SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training
Authors: Wei Han, Soujanya Poria Hui Chen
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Self-training methods have been explored in recent years and have exhibited great performance in improving semi-supervised learning. This work presents a simple instance-adaptive self-training method (SAT) for semi-supervised text classification. SAT first generates two augmented views for each unlabeled data, and then trains a meta learner to automatically identify the relative strength of augmentations based on the similarity between the original view and the augmented views. The weakly-augmented view is fed to the model to produce a pseudo-label and the strongly-augmented view is used to train the model to predict the same pseudo-label. We conducted extensive experiments and analyses on three text classification datasets and found that with varying sizes of labeled training data, SAT consistently shows competitive performance compared to existing semi-supervised learning methods.
Answer Quality Aware Aggregation for Extractive QA Crowdsourcing
Authors: Zhen Wang, Claudia Hauff, Jie Yang, Avishek Anand Peide Zhu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Quality control is essential for creating extractive question answering (EQA) datasets via crowdsourcing. Aggregation across answers, i.e. word spans within passages annotated, by different crowd workers is one major focus for ensuring its quality. However, crowd workers cannot reach a consensus on a considerable portion of questions. We introduce a simple yet effective answer aggregation method that takes into account the relations among the answer, question, and context passage. We evaluate answer quality from both the view of question answering model to determine how confident the QA model is about each answer and the view of the answer verification model to determine whether the answer is correct. Then we compute aggregation scores with each answer`s quality and its contextual embedding produced by pre-trained language models. The experiments on a large real crowdsourced EQA dataset show that our framework outperforms baselines by around 16% on precision and effectively conduct answer aggregation for extractive QA task.
Search to Pass Messages for Temporal Knowledge Graph Completion
Authors: Haotong Du, Quanming Yao, Xuelong Li Zhen Wang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Completing missing facts is a fundamental task for temporal knowledge graphs (TKGs).Recently, graph neural network (GNN) based methods, which can simultaneously explore topological and temporal information, have become the state-of-the-art (SOTA) to complete TKGs. However, these studies are based on hand-designed architectures and fail to explore the diverse topological and temporal properties of TKG.To address this issue, we propose to use neural architecture search (NAS) to design data-specific message passing architecture for TKG completion.In particular, we develop a generalized framework to explore topological and temporal information in TKGs.Based on this framework, we design an expressive search space to fully capture various properties of different TKGs. Meanwhile, we adopt a search algorithm, which trains a supernet structure by sampling single path for efficient search with less cost.We further conduct extensive experiments on three benchmark datasets. The results show that the searched architectures by our method achieve the SOTA performances.Besides, the searched models can also implicitly reveal diverse properties in different TKGs.Our code is released in https://github.com/striderdu/SPA.
Code Vulnerability Detection via Nearest Neighbor Mechanism
Authors: Xiaohui Kuang, Gang Zhao Qianjin Du
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Code vulnerability detection is a fundamental and challenging task in the software security field. Existing research works aim to learn semantic information from the source code by utilizing NLP technologies. However, in vulnerability detection tasks, some vulnerable samples are very similar to non-vulnerable samples, which are difficult to identify. To address this issue and improve detection performance, we introduce the $k$-nearest neighbor mechanism which retrieves multiple neighbor samples and utilizes label information of retrieved neighbor samples to provide help for model predictions. Besides, we use supervised contrastive learning to make the model learn the discriminative representation and ensure that label information of retrieved neighbor samples is as consistent as possible with the label information of testing samples. Extensive experiments show that our method can achieve obvious performance improvements compared to baseline models.
Robust Question Answering against Distribution Shifts with Test-Time Adaption: An Empirical Study
Authors: Yuyang Ding, Juntao Li, Hwee Tou Ng Hai Ye
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: A deployed question answering (QA) model can easily fail when the test data has a distribution shift compared to the training data. Robustness tuning (RT) methods have been widely studied to enhance model robustness against distribution shifts before model deployment. However, can we improve a model after deployment? To answer this question, we evaluate test-time adaptation (TTA) to improve a model after deployment. We first introduce ColdQA, a unified evaluation benchmark for robust QA against text corruption and changes in language and domain. We then evaluate previous TTA methods on ColdQA and compare them to RT methods. We also propose a novel TTA method called online imitation learning (OIL). Through extensive experiments, we find that TTA is comparable to RT methods, and applying TTA after RT can significantly boost the performance on ColdQA. Our proposed OIL improves TTA to be more robust to variation in hyper-parameters and test distributions over time.
ParaMac: A General Unsupervised Paraphrase Generation Framework Leveraging Semantic Constraints and Diversifying Mechanisms
Authors: Jiaxin Shi, Ji Qi, Lei Hou, Juanzi Li, Qi Tian Jinxin Liu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Paraphrase generation reflects the ability to understand the meaning from the language surface form and rephrase it to other expressions. Recent paraphrase generation works have paid attention to unsupervised approaches based on Pre-trained Language Models (PLMs) to avoid heavy reliance on parallel data by utilizing PLMs' generation ability. However, the generated pairs of existing unsupervised methods are usually weak either in semantic equivalence or expression diversity. In this paper, we present a novel unsupervised paraphrase generation framework called Paraphrase Machine. By employing multi-aspect equivalence constraints and multi-granularity diversifying mechanisms, Paraphrase Machine is able to achieve good semantic equivalence and expressive diversity, producing a high-quality unsupervised paraphrase dataset. Based on this dataset, we train a general paraphrase model, which can be directly applied to rewrite the input sentence of various domains without any fine-tuning, and achieves substantial gains of 9.1% and 3.3% absolutely in BLEU score over previous SOTA on Quora and MSCOCO. By further fine-tuning our model with domain-specific training sets, the improvement can be increased to even 18.0% and 4.6%. Most importantly, by applying it to language understanding and generation tasks under the low-resource setting, we demonstrate that our model can serve as a universal data augmentor to boost the few-shot performance (e.g., average 2.0% gain on GLUE).
Semi-supervised New Slot Discovery with Incremental Clustering
Authors: Lizi Liao, Xueming Qian, Tat-Seng Chua Yuxia Wu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Discovering new slots is critical to the success of dialogue systems. Most existing methods rely on automatic slot induction in unsupervised fashion or perform domain adaptation across zero or few-shot scenarios. They have difficulties in providing high-quality supervised signals to learn clustering-friendly features, and are limited in effectively transferring the prior knowledge from known slots to new slots. In this work, we propose a Semi-supervised Incremental Clustering method (SIC), to discover new slots with the aid of existing linguistic annotation models and limited known slot data. Specifically, we harvest slot value candidates with NLP model cues and innovatively formulate the slot discovery task under an incremental clustering framework. The model gradually calibrate slot representations under the supervision of generated pseudo-labels, and automatically learns to terminate when no more salient slot remains. Our thorough evaluation on five public datasets demonstrates that it significantly outperforms state-of-the-art models.
Con-NAT: Contrastive Non-autoregressive Neural Machine Translation
Authors: Zhihua Zhang Hao Cheng
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Inspired by the success of contrastive learning in natural language processing, we incorporate contrastive learning into the conditional masked language model which is extensively used in non-autoregressive neural machine translation (NAT). Accordingly, we propose a Contrastive Non-autoregressive Neural Machine Translation (Con-NAT) model. Con-NAT optimizes the similarity of several different representations of the same token in the same sentence. We propose two methods to obtain various representations: Contrastive Common Mask and Contrastive Dropout. Positive pairs are various different representations of the same token, while negative pairs are representations of different tokens. In the feature space, the model with contrastive loss pulls positive pairs together and pushes negative pairs away. We conduct extensive experiments on six translation directions with different data sizes. The results demonstrate that Con-NAT showed a consistent and significant improvement in fully and iterative NAT. Con-NAT is state-of-the-art on WMT`16 Ro-En (34.18 BLEU).
Improved Knowledge Distillation for Pre-trained Language Models via Knowledge Selection
Authors: Yi Lu, Yongyu Mu, Yimin Hu, Tong Xiao, Jingbo Zhu Chenglong Wang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Knowledge distillation addresses the problem of transferring knowledge from a teacher model to a student model.In this process, we typically have multiple types of knowledge extracted from the teacher model.The problem is to make full use of them to train the student model.Our preliminary study shows that: (1) not all of the knowledge is necessary for learning a good student model, and (2) knowledge distillation can benefit from certain knowledge at different training steps.In response to these, we propose an actor-critic approach to selecting appropriate knowledge to transfer during the process of knowledge distillation.In addition, we offer a refinement of the training algorithm to ease the computational burden.Experimental results on the GLUE datasets show that our method outperforms several strong knowledge distillation baselines significantly.
Syntactically Robust Training on Partially-Observed Data for Open Information Extraction
Authors: Yuxiang Chen, Lei Hou, Juanzi Li, Bin Xu Ji Qi
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Open Information Extraction models have shown promising results with sufficient supervision. However, these models face a fundamental challenge that the syntactic distribution of training data is partially observable in comparison to the real world. In this paper, we propose a syntactically robust training framework that enables models to be trained on a syntactic-abundant distribution based on diverse paraphrase generation. To tackle the intrinsic problem of knowledge deformation of paraphrasing, two algorithms based on semantic similarity matching and syntactic tree walking are used to restore the expressionally transformed knowledge. The training framework can be generally applied to other syntactic partial observable domains. Based on the proposed framework, we build a new evaluation set called CaRB-AutoPara, a syntactically diverse dataset consistent with the real-world setting for validating the robustness of the models. Experiments including a thorough analysis show that the performance of the model degrades with the increase of the difference in syntactic distribution, while our framework gives a robust boundary.
A Benchmark and Dataset for Post-OCR text correction in Sanskrit
Authors: Nikhil Singh, Amrith Krishna, Ganesh Ramakrishnan Ayush Maheshwari
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Sanskrit is a classical language with about 30 million extant manuscripts fit for digitisation, available in written, printed or scanned-image forms. However, it is still considered to be a low-resource language when it comes to available digital resources. In this work, we release a post-OCR text correction dataset containing around 218,000 sentences, with 1.5 million words, from 30 different books. Texts in Sanskrit are known to be diverse in terms of their linguistic and stylistic usage since Sanskrit was the \textquoteleftlingua francua' for discourse in the Indian subcontinent for about 3 millennia. Keeping this in mind, we release a multi-domain dataset, from areas as diverse as astronomy, medicine and mathematics, with some of them as old as 18 centuries. Further, we release multiple strong baselines as benchmarks for the task, based on pre-trained Seq2Seq language models. We find that our best-performing model, consisting of byte level tokenization in conjunction with phonetic encoding (Byt5+SLP1), yields a 23% point increase over the OCR output in terms of word and character error rates. Moreover, we perform extensive experiments in evaluating these models on their performance and analyse common causes of mispredictions both at the graphemic and lexical levels. Our code and dataset is publicly available at https://github.com/ayushbits/pe-ocr-sanskrit.
Knowledge-Enhanced Self-Supervised Prototypical Network for Few-Shot Event Detection
Authors: Xiaolong Jin, Long Bai, Jiafeng Guo, Xueqi Cheng Kailin Zhao
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Prototypical network based joint methods have attracted much attention in few-shot event detection, which carry out event detection in a unified sequence tagging framework. However, these methods suffer from the inaccurate prototype representation problem, due to two main reasons: the number of instances for calculating prototypes is limited; And, they do not well capture the relationships among event prototypes. To deal with this problem, we propose a Knowledge-Enhanced self-supervised Prototypical Network, called KE-PN, for few-shot event detection. KE-PN adopts hybrid rules, which can automatically align event types to an external knowledge base, i.e., FrameNet, to obtain more instances.It proposes a self-supervised learning method to filter out noisy data from enhanced instances. KE-PN is further equipped with an auxiliary event type relationship classification module, which injects the relationship information into representations of event prototypes. Extensive experiments on three benchmark datasets, i.e., FewEvent, MAVEN, and ACE2005 demonstrate the state-of-the-art performance of KE-PN.
VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding
Authors: Xiaolong Hou, Xiyang Du, Mengyuan Zhou, Lianxin Jiang, Yang Mo, Xiaofeng Shi Dou Hu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Pre-trained language models have been widely applied to standard benchmarks. Due to the flexibility of natural language, the available resources in a certain domain can be restricted to support obtaining precise representation. To address this issue, we propose a novel Transformer-based language model named VarMAE for domain-adaptive language understanding. Under the masked autoencoding objective, we design a context uncertainty learning module to encode the token`s context into a smooth latent distribution. The module can produce diverse and well-formed contextual representations. Experiments on science- and finance-domain NLU tasks demonstrate that VarMAE can be efficiently adapted to new domains with limited resources.
Exploring Methods for Building Dialects-Mandarin Code-Mixing Corpora: A Case Study in Taiwanese Hokkien
Authors: Bo-Han Lu, Chao-Yi Lu, Richard Tzong-Han Tsai Sin-En Lu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: In natural language processing (NLP), code-mixing (CM) is a challenging task, especially when the mixed languages include dialects. In Southeast Asian countries such as Singapore, Indonesia, and Malaysia, Hokkien-Mandarin is the most widespread code-mixed language pair among Chinese immigrants, and it is also common in Taiwan. However, dialects such as Hokkien often have a scarcity of resources and the lack of an official writing system, limiting the development of dialect CM research. In this paper, we propose a method to construct a Hokkien-Mandarin CM dataset to mitigate the limitation, overcome the morphological issue under the Sino-Tibetan language family, and offer an efficient Hokkien word segmentation method through a linguistics-based toolkit. Furthermore, we use our proposed dataset and employ transfer learning to train the XLM (cross-lingual language model) for translation tasks. To fit the code-mixing scenario, we adapt XLM slightly. We found that by using linguistic knowledge, rules, and language tags, the model produces good results on CM data translation while maintaining monolingual translation quality.
Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in Transformer-Based Variational AutoEncoder for Diverse Text Generation
Authors: Xiaoyuan Yi, Wenhao Li, Maosong Sun, Xing Xie Jinyi Hu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Variational Auto-Encoder (VAE) has been widely adopted in text generation. Among many variants, recurrent VAE learns token-wise latent variables with each conditioned on the preceding ones, which captures sequential variability better in the era of RNN. However, it is unclear how to incorporate such recurrent dynamics into the recently dominant Transformer due to its parallelism. In this work, we propose TRACE, a Transformer-based recurrent VAE structure. TRACE imposes recurrence on segment-wise latent variables with arbitrarily separated text segments and constructs the posterior distribution with residual parameterization. Besides, we design an acceleration method by approximating idempotent matrices, which allows parallelism while maintaining the conditional dependence of latent variables. We demonstrate that TRACE could deduce a non-zero lower bound of the KL term and enhance the entanglement of each segment and preceding latent variables, providing a theoretical guarantee of generation diversity. Experiments on two unconditional and one conditional generation task show that TRACE achieves significantly improved diversity while maintaining satisfactory generation quality.
Tweet Based Reach Aware Temporal Attention Network for NFT Valuation
Authors: Megh Thakkar, Ritesh Soun, Atula Neerkaje, Vasu Sharma, Dipanwita Guhathakurta, Sudheer Chava Ramit Sawhney
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Non-Fungible Tokens (NFTs) are a relatively unexplored class of assets. Designing strategies to forecast NFT trends is an intricate task due to its extremely volatile nature. The market is largely driven by public sentiment and “hype”, which in turn has a high correlation with conversations taking place on social media platforms like Twitter. Prior work done for modelling stock market data does not take into account the extent of impact certain highly influential tweets and their authors can have on the market. Building on these limitations and the nature of the NFT market, we propose a novel reach-aware temporal learning approach to make predictions for forecasting future trends in the NFT market. We perform experiments on a new dataset consisting of over 1.3 million tweets and 180 thousand NFT transactions spanning over 15 NFT collections curated by us. Our model (TA-NFT) outperforms other state-of-the-art methods by an average of 36%. Through extensive quantitative and ablative analysis, we demonstrate the ability of our approach as a practical method for predicting NFT trends.
Entity Embedding Completion for Wide-Coverage Entity Disambiguation
Authors: Ikuya Yamada, Naoki Yoshinaga, Masashi Toyoda Daisuke Oba
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Entity disambiguation (ED) is typically solved by learning to classify a given mention into one of the entities in the model`s entity vocabulary by referring to their embeddings. However, this approach cannot address mentions of entities that are not covered by the entity vocabulary. Aiming to enhance the applicability of ED models, we propose a method of extending a state-of-the-art ED model by dynamically computing embeddings of out-of-vocabulary entities. Specifically, our method computes embeddings from entity descriptions and mention contexts. Experiments with standard benchmark datasets show that the extended model performs comparable to or better than existing models whose entity embeddings are trained for all candidate entities as well as embedding-free models. We release our source code and model checkpoints at https://github.com/studio-ousia/steel.
Entity-level Interaction via Heterogeneous Graph for Multimodal Named Entity Recognition
Authors: Guanting Dong, Yidong Shi, Haolong Yan, Weiran Xu, Si Li Gang Zhao
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Multimodal Named Entity Recognition (MNER) faces two specific challenges: 1) How to capture useful entity-related visual information. 2) How to alleviate the interference of visual noise. Previous works have gained progress by improving interacting mechanisms or seeking for better visual features. However, existing methods neglect the integrity of entity semantics and conduct cross-modal interaction at token-level, which cuts apart the semantics of entities and makes non-entity tokens easily interfered with by irrelevant visual noise. Thus in this paper, we propose an end-to-end heterogeneous Graph-based Entity-level Interacting model (GEI) for MNER. GEI first utilizes a span detection subtask to obtain entity representations, which serve as the bridge between two modalities. Then, the heterogeneous graph interacting network interacts entity with object nodes to capture entity-related visual information, and fuses it into only entity-associated tokens to rid non-entity tokens of the visual noise. Experiments on two widely used datasets demonstrate the effectiveness of our method. Our code will be available at https://github.com/GangZhao98/GEI.
Status Biases in Deliberation Online: Evidence from a Randomized Experiment on ChangeMyView
Authors: Yohan Jo, Alan Montgomery Emaad Manzoor
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Status is widely used to incentivize user engagement online. However, visible status indicators could inadvertently bias online deliberation to favor high-status users. In this work, we design and deploy a randomized experiment on the ChangeMyView platform to quantify status biases in deliberation online. We find strong evidence of status bias: hiding status on ChangeMyView increases the persuasion rate of moderate-status users by 84% and decreases the persuasion rate of high-status users by 41% relative to the control group. We also find that the persuasive power of status is moderated by verbosity, suggesting that status is used as an information-processing heuristic under cognitive load. Finally, we find that a user`s status influences the argumentation behavior of other users they interact with in a manner that disadvantages low and moderate-status users.
Empathetic and Emotionally Positive Conversation Systems with an Emotion-specific Query-Response Memory
Authors: Yinliang Wang, Yiping Song, Chi Zhang, Dongkyu Lee, Yingxiu Zhao, Dongsheng Li, Nevin L. Zhang Zhiliang Tian
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Emotional conversation systems generate responses for the input queries considering the speaker`s emotions in a conversation. Existing emotional conversation systems output emotional responses according to either a given emotion or the user`s emotion reflected in the input queries. Following a given emotion may lead to an emotional drift between the given emotion and the conversation state, and following only the user`s emotion may aggravate the user`s negative feelings if users suffer from a negative mood. In this paper, we propose to generate empathetic responses catering to the user`s emotions while leading the conversation to be emotionally positive. Particularly, by abstracting the conversation corpus, we extract and store the different responding strategies for different users' emotions and conversational topics into a memory. We encourage positive emotions in conversation via a sentiment evaluator. We model the memory outputs with a Gaussian mixture distribution and sample a final responding strategy from the distribution. The strategy acts as a condition to a transformer model to generate responses. The experiments verify our model surpasses the baseline methods in appropriateness, diversity, and generating emotionally positive responses.
Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision
Authors: Jimeng Sun Zifeng Wang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Clinical trials are essential for drug development but are extremely expensive and time-consuming to conduct. It is beneficial to study similar historical trials when designing a clinical trial. However, lengthy trial documents and lack of labeled data make trial similarity search difficult. We propose a zero-shotclinical trial retrieval method, called Trial2Vec, which learns through self-supervision without the need for annotating similar clinical trials. Specifically, the meta-structure of trial documents (e.g., title, eligibility criteria, target disease) along with clinical knowledge (e.g., UMLS knowledge base) are leveraged to automatically generate contrastive samples. Besides, encodes trial documents considering meta-structure thus producing compact embeddings aggregating multi-aspect information from the whole document. We show that our method yields medically interpretable embeddings by visualization and it gets 15% average improvement over the best baselines on precision/recall for trial retrieval, which is evaluated on our labeled 1600 trial pairs. In addition, we prove the pretrained embeddings benefit the downstream trial outcome prediction task over 240k trials. Software is available at https://github.com/RyanWangZf/Trial2Vec.
From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models
Authors: Yankai Lin, Xuancheng Ren, Guangxiang Zhao, Peng Li, Jie Zhou, Xu Sun Lei Li
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Investigating better ways to reuse the released pre-trained language models (PLMs) can significantly reduce the computational cost and the potential environmental side-effects. This paper explores a novel PLM reuse paradigm, Knowledge Integration (KI). Without human annotations available, KI aims to merge the knowledge from different teacher-PLMs, each of which specializes in a different classification problem, into a versatile student model. To achieve this, we first derive the correlation between virtual golden supervision and teacher predictions. We then design a Model Uncertainty--aware Knowledge Integration (MUKI) framework to recover the golden supervision for the student. Specifically, MUKI adopts Monte-Carlo Dropout to estimate model uncertainty for the supervision integration. An instance-wise re-weighting mechanism based on the margin of uncertainty scores is further incorporated, to deal with the potential conflicting supervision from teachers.Experimental results demonstrate that MUKI achieves substantial improvements over baselines on benchmark datasets. Further analysis shows that MUKI can generalize well for merging teacher models with heterogeneous architectures, and even teachers major in cross-lingual datasets.
Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings
Authors: Rodrigo Agerri, German Rigau Iker García-Ferrero
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Zero-resource cross-lingual transfer approaches aim to apply supervised modelsfrom a source language to unlabelled target languages. In this paper we performan in-depth study of the two main techniques employed so far for cross-lingualzero-resource sequence labelling, based either on data or model transfer.Although previous research has proposed translation and annotation projection(data-based cross-lingual transfer) as an effective technique for cross-lingualsequence labelling, in this paper we experimentally demonstrate that highcapacity multilingual language models applied in a zero-shot (model-basedcross-lingual transfer) setting consistently outperform data-basedcross-lingual transfer approaches. A detailed analysis of our results suggeststhat this might be due to important differences in language use. Morespecifically, machine translation often generates a textual signal which isdifferent to what the models are exposed to when using gold standard data,which affects both the fine-tuning and evaluation processes. Our results alsoindicate that data-based cross-lingual transfer approaches remain a competitiveoption when high-capacity multilingual language models are not available.
Early Guessing for Dialect Identification
Authors: Tanja Samardzic, Fabio Rinaldi, Ljiljana Dolamic Vani Kanjirangat
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: This paper deals with the problem of incre-mental dialect identification. Our goal is toreliably determine the dialect before the fullutterance is given as input. The major partof the previous research on dialect identification has been model-centric, focusing on performance. We address a new question: How much input is needed to identify a dialect? Ourapproach is a data-centric analysis that resultsin general criteria for finding the shortest inputneeded to make a plausible guess. Workingwith three sets of language dialects (Swiss German, Indo-Aryan and Arabic languages), weshow that it is possible to generalize across dialects and datasets with two input shorteningcriteria: model confidence and minimal inputlength (adjusted for the input type). The sourcecode for experimental analysis can be found atGithub.
R-AT: Regularized Adversarial Training for Natural Language Understanding
Authors: Jiawen Li, Hung-Yu Kao Shiwen Ni
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Currently, adversarial training has become a popular and powerful regularization method in the natural language domain. In this paper, we Regularized Adversarial Training (R-AT) via dropout, which forces the output probability distributions of different sub-models generated by dropout to be consistent under the same adversarial samples. Specifically, we generate adversarial samples by perturbing the word embeddings. For each adversarial sample fed to the model, R-AT minimizes both the adversarial risk and the bidirectional KL-divergence between the adversarial output distributions of two sub-models sampled by dropout. Through extensive experiments on 13 public natural language understanding datasets, we found that R-AT has improvements for many models (e.g., rnn-based, cnn-based, and transformer-based models). For the GLUE benchmark, when R-AT is only applied to the fine-tuning stage, it is able to improve the overall test score of the BERT-base model from 78.3 to 79.6 and the RoBERTa-large model from 88.1 to 88.6. Theoretical analysis reveals that R-AT has potential gradient regularization during the training process. Furthermore, R-AT can reduce the inconsistency between training and testing of models with dropout.
Multi-View Active Learning for Short Text Classification in User-Generated Data
Authors: Negin Karisani, Li Xiong Payam Karisani
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Mining user-generated data often suffers from the lack of enough labeled data, short document lengths, and the informal user language. In this paper, we propose a novel active learning model to overcome these obstacles in the tasks tailored for query phrases--e.g., detecting positive reports of natural disasters. Our model has three novelties: 1) It is the first approach to employ multi-view active learning in this domain. 2) It uses the Parzen-Rosenblatt window method to integrate the representativeness measure into multi-view active learning. 3) It employs a query-by-committee strategy, based on the agreement between predictors, to address the usually noisy language of the documents in this domain. We evaluate our model in four publicly available Twitter datasets with distinctly different applications. We also compare our model with a wide range of baselines including those with multiple classifiers. The experiments testify that our model is highly consistent and outperforms existing models.
Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning
Authors: Ruixue Ding, Hai Zhao, Boli Chen, Pengjun Xie, Fei Huang, Min Zhang Hongqiu Wu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Multiple pre-training objectives fill the vacancy of the understanding capability of single-objective language modeling, which serves the ultimate purpose of pre-trained language models (PrLMs), generalizing well on a mass of scenarios. However, learning multiple training objectives in a single model is challenging due to the unknown relative significance as well as the potential contrariety between them. Empirical studies have shown that the current objective sampling in an ad-hoc manual setting makes the learned language representation barely converge to the desired optimum. Thus, we propose \textitMOMETAS, a novel adaptive sampler based on meta-learning, which learns the latent sampling pattern on arbitrary pre-training objectives. Such a design is lightweight with negligible additional training overhead. To validate our approach, we adopt five objectives and conduct continual pre-training with BERT-base and BERT-large models, where MOMETAS demonstrates universal performance gain over other rule-based sampling strategies on 14 natural language processing tasks.
ConGen: Unsupervised Control and Generalization Distillation For Sentence Representation
Authors: Wuttikorn Ponwitayarat, Lalita Lowphansirikul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong Peerat Limkonchotiwat
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Sentence representations are essential in many NLP tasks operating at the sentence level.Recently, research attention has shifted towards learning how to represent sentences without any annotations, i.e., unsupervised representation learning. Despite the benefit of training without supervised data, there is still a performance penalty compared to supervised methods.Furthermore, the supervised-unsupervised performance gap widens as we reduce the model size. In this paper, we propose an unsupervised sentence representation method to reduce the supervised-unsupervised performance gap, especially for smaller models. Utilizing the concept for knowledge distillation, we derive a distillation framework comprising two training objectives, control and generalize, called ConGen. Experiments on semantic textual similarity (STS), text classification (transfer), and natural language inference (NLI) tasks show that ConGen is on par with supervised training even on smaller models.Furthermore, our method consistently outperformed competitors on multilingual STS.The code and models are available at https://github.com/KornWtp/ConGen.
Large-Scale Differentially Private BERT
Authors: Badih Ghazi, Vineet Gupta, Ravi Kumar, Pasin Manurangsi Rohan Anil
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: In this work, we study the large-scale pretraining of BERT-Large (Devlin et al., 2019) with differentially private SGD (DP-SGD). We show that combined with a careful implementation, scaling up the batch size to millions (i.e., mega-batches) improves the utility of the DP-SGD step for BERT; we also enhance the training efficiency by using an increasing batch size schedule. Our implementation builds on the recent work of Subramani et al (2020), who demonstrated that the overhead of a DP-SGD step is minimized with effective use of JAX (Bradbury et al., 2018; Frostig et al., 2018) primitives in conjunction with the XLA compiler (XLA team and collaborators, 2017). Our implementation achieves a masked language model accuracy of 60.5% at a batch size of 2M, for epsilon=5, which is a reasonable privacy setting. To put this number in perspective, non-private BERT models achieve an accuracy of \ensuremath∼70%.
Improving Zero-Shot Multilingual Translation with Universal Representations and Cross-Mapping
Authors: Yang Feng Shuhao Gu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: The many-to-many multilingual neural machine translation can translate between language pairs unseen during training, i.e., zero-shot translation. Improving zero-shot translation requires the model to learn universal representations and cross-mapping relationships to transfer the knowledge learned on the supervised directions to the zero-shot directions. In this work, we propose the state mover`s distance based on the optimal theory to model the difference of the representations output by the encoder. Then, we bridge the gap between the semantic-equivalent representations of different languages at the token level by minimizing the proposed distance to learn universal representations. Besides, we propose an agreement-based training scheme, which can help the model make consistent predictions based on the semantic-equivalent sentences to learn universal cross-mapping relationships for all translation directions. The experimental results on diverse multilingual datasets show that our method can improve consistently compared with the baseline system and other contrast methods. The analysis proves that our method can better align the semantic space and improve the prediction consistency.
Controllable Fake Document Infilling for Cyber Deception
Authors: Yu Lin, Erick Skorupa Parolin, Latifur Khan, Kevin Hamlen Yibo Hu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Recent works in cyber deception study how to deter malicious intrusion by generating multiple fake versions of a critical document to impose costs on adversaries who need to identify the correct information. However, existing approaches are context-agnostic, resulting in sub-optimal and unvaried outputs. We propose a novel context-aware model, Fake Document Infilling (FDI), by converting the problem to a controllable mask-then-infill procedure. FDI masks important concepts of varied lengths in the document, then infills a realistic but fake alternative considering both the previous and future contexts. We conduct comprehensive evaluations on technical documents and news stories. Results show that FDI outperforms the baselines in generating highly believable fakes with moderate modification to protect critical information and deceive adversaries.
Weakly Supervised Headline Dependency Parsing
Authors: Tianze Shi, Ozan İrsoy, Igor Malioutov Adrian Benton
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: English news headlines form a register with unique syntactic properties that have been documented in linguistics literature since the 1930s. However, headlines have received surprisingly little attention from the NLP syntactic parsing community. We aim to bridge this gap by providing the first news headline corpus of Universal Dependencies annotated syntactic dependency trees, which enables us to evaluate existing state-of-the-art dependency parsers on news headlines. To improve English news headline parsing accuracies, we develop a projection method to bootstrap silver training data from unlabeled news headline-article lead sentence pairs. Models trained on silver headline parses demonstrate significant improvements in performance over models trained solely on gold-annotated long-form texts. Ultimately, we find that, although projected silver training data improves parser performance across different news outlets, the improvement is moderated by constructions idiosyncratic to outlet.
BOOKSUM: A Collection of Datasets for Long-form Narrative Summarization
Authors: Nazneen Rajani, Divyansh Agarwal, Caiming Xiong, Dragomir Radev Wojciech Kryscinski
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: The majority of existing text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and stylistic biases. While relevant, such datasets will offer limited challenges for future text summarization systems. We address these issues by introducing BOOKSUM, a collection of datasets for long-form narrative summarization. Our dataset covers documents from the literature domain, such as novels, plays and stories, and includes highly abstractive, human written summaries on three levels of granularity of increasing difficulty: paragraph-, chapter-, and book-level. The domain and structure of our dataset poses a unique set of challenges for summarization systems, which include: processing very long documents, non-trivial causal and temporal dependencies, and rich discourse structures. To facilitate future work, we trained and evaluated multiple extractive and abstractive summarization models as baselines for our dataset.
Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis
Authors: Yi-Lin Tuan, Yujie Lu, Michael Saxon, Lei Li, William Yang Wang Wenda Xu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 (Findings Track), 2022

Abstract: Is it possible to build a general and automatic natural language generation (NLG) evaluation metric? Existing learned metrics either perform unsatisfactorily or are restricted to tasks where large human rating data is already available. We introduce SESCORE, a model-based metric that is highly correlated with human judgements without requiring human annotation, by utilizing a novel, iterative error synthesis and severity scoring pipeline. This pipeline applies a series of plausible errors to raw text and assigns severity labels by simulating human judgements with entailment. We evaluate SESCORE against existing metrics by comparing how their scores correlate with human ratings. SESCORE outperforms all prior unsupervised metrics on multiple diverse NLG tasks including machine translation, image captioning, and WebNLG text generation. For WMT 20/21En-De and Zh-En, SESCORE improve the average Kendall correlation with human judgement from 0.154 to 0.195. SESCORE even achieves comparable performance to the best supervised metric COMET, despite receiving no human annotated training data.