mtybilly/Instruct2DS huggingface dataset:
Bernice: A Multilingual Pre-trained Encoder for Twitter
Authors: Alexandra DeLucia, Shijie Wu, Aaron Mueller, Carlos Aguirre, Philip Resnik, Mark Dredze
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: The language of Twitter differs significantly from that of other domains commonly included in large language model training. While tweets are typically multilingual and contain informal language, including emoji and hashtags, most pre-trained language models for Twitter are either monolingual, adapted from other domains rather than trained exclusively on Twitter, or are trained on a limited amount of in-domain Twitter data.We introduce Bernice, the first multilingual RoBERTa language model trained from scratch on 2.5 billion tweets with a custom tweet-focused tokenizer. We evaluate on a variety of monolingual and multilingual Twitter benchmarks, finding that our model consistently exceeds or matches the performance of a variety of models adapted to social media data as well as strong multilingual baselines, despite being trained on less data overall.We posit that it is more efficient compute- and data-wise to train completely on in-domain data with a specialized domain-specific tokenizer.
CEFR-Based Sentence Difficulty Annotation and Assessment
Authors: Yuki Arase, Satoru Uchida, Tomoyuki Kajiwara
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Controllable text simplification is a crucial assistive technique for language learning and teaching. One of the primary factors hindering its advancement is the lack of a corpus annotated with sentence difficulty levels based on language ability descriptions. To address this problem, we created the CEFR-based Sentence Profile (CEFR-SP) corpus, containing 17k English sentences annotated with the levels based on the Common European Framework of Reference for Languages assigned by English-education professionals. In addition, we propose a sentence-level assessment model to handle unbalanced level distribution because the most basic and highly proficient sentences are naturally scarce. In the experiments in this study, our method achieved a macro-F1 score of 84.5% in the level assessment, thus outperforming strong baselines employed in readability assessment.
Simple Questions Generate Named Entity Recognition Datasets
Authors: Hyunjae Kim, Jaehyo Yoo, Seunghyun Yoon, Jinhyuk Lee, Jaewoo Kang
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Recent named entity recognition (NER) models often rely on human-annotated datasets requiring the vast engagement of professional knowledge on the target domain and entities. This work introduces an ask-to-generate approach, which automatically generates NER datasets by asking simple natural language questions to an open-domain question answering system (e.g., “Which disease?”). Despite using fewer training resources, our models solely trained on the generated datasets largely outperform strong low-resource models by 19.5 F1 score across six popular NER benchmarks. Our models also show competitive performance with rich-resource models that additionally leverage in-domain dictionaries provided by domain experts. In few-shot NER, we outperform the previous best model by 5.2 F1 score on three benchmarks and achieve new state-of-the-art performance.
TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models
Authors: Joel Jang, Seonghyeon Ye, Changho Lee, Sohee Yang, Joongbo Shin, Janghoon Han, Gyeonghun Kim, Minjoon Seo
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Language Models (LMs) become outdated as the world changes; they often fail to perform tasks requiring recent factual information which was absent or different during training, a phenomenon called temporal misalignment. This is especially a challenging problem because the research community still lacks a coherent dataset for assessing the adaptability of LMs to frequently-updated knowledge corpus such as Wikipedia. To this end, we introduce TemporalWiki, a lifelong benchmark for ever-evolving LMs that utilizes the difference between consecutive snapshots of English Wikipedia and English Wikidata for training and evaluation, respectively. The benchmark hence allows researchers to periodically track an LM`s ability to retain previous knowledge and acquire updated/new knowledge at each point in time. We also find that training an LM on the diff data through continual learning methods achieves similar or better perplexity than on the entire snapshot in our benchmark with 12 times less computational cost, which verifies that factual knowledge in LMs can be safely updated with minimal training data via continual learning.
Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction
Authors: Lu Dai, Bang Wang, Wei Xiang, Yijun Mo
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Recently, prompt-tuning has attracted growing interests in event argument extraction (EAE). However, the existing prompt-tuning methods have not achieved satisfactory performance due to the lack of consideration of entity information. In this paper, we propose a bi-directional iterative prompt-tuning method for EAE, where the EAE task is treated as a cloze-style task to take full advantage of entity information and pre-trained language models (PLMs). Furthermore, our method explores event argument interactions by introducing the argument roles of contextual entities into prompt construction. Since template and verbalizer are two crucial components in a cloze-style prompt, we propose to utilize the role label semantic knowledge to construct a semantic verbalizer and design three kind of templates for the EAE task. Experiments on the ACE 2005 English dataset with standard and low-resource settings show that the proposed method significantly outperforms the peer state-of-the-art methods.
Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation
Authors: Peiyi Wang, Yifan Song, Tianyu Liu, Binghuai Lin, Yunbo Cao, Sujian Li, Zhifang Sui
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Continual relation extraction (CRE) aims to continually learn new relations from a class-incremental data stream. CRE model usually suffers from catastrophic forgetting problem, i.e., the performance of old relations seriously degrades when the model learns new relations. Most previous work attributes catastrophic forgetting to the corruption of the learned representations as new relations come, with an implicit assumption that the CRE models have adequately learned the old relations. In this paper, through empirical studies we argue that this assumption may not hold, and an important reason for catastrophic forgetting is that the learned representations do not have good robustness against the appearance of analogous relations in the subsequent learning process. To address this issue, we encourage the model to learn more precise and robust representations through a simple yet effective adversarial class augmentation mechanism (ACA), which is easy to implement and model-agnostic.Experimental results show that ACA can consistently improve the performance of state-of-the-art CRE models on two popular benchmarks.
ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering
Authors: Zhiyu Chen, Shiyang Li, Charese Smiley, Zhiqiang Ma, Sameena Shah, William Yang Wang
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: With the recent advance in large pre-trained language models, researchers have achieved record performances in NLP tasks that mostly focus on language pattern matching. The community is experiencing the shift of the challenge from how to model language to the imitation of complex reasoning abilities like human beings. In this work, we investigate the application domain of finance that involves real-world, complex numerical reasoning. We propose a new large-scale dataset, ConvFinQA, aiming to study the chain of numerical reasoning in conversational question answering. Our dataset poses great challenge in modeling long-range, complex numerical reasoning paths in real-world conversations. We conduct comprehensive experiments and analyses with both the neural symbolic methods and the prompting-based methods, to provide insights into the reasoning mechanisms of these two divisions. We believe our new dataset should serve as a valuable resource to push forward the exploration of real-world, complex reasoning tasks as the next research focus. Our dataset and code is publicly available at https://github.com/czyssrs/ConvFinQA.
A Span-based Multimodal Variational Autoencoder for Semi-supervised Multimodal Named Entity Recognition
Authors: Baohang Zhou, Ying Zhang, Kehui Song, Wenya Guo, Guoqing Zhao, Hongbin Wang, Xiaojie Yuan
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Multimodal named entity recognition (MNER) on social media is a challenging task which aims to extract named entities in free text and incorporate images to classify them into user-defined types. However, the annotation for named entities on social media demands a mount of human efforts. The existing semi-supervised named entity recognition methods focus on the text modal and are utilized to reduce labeling costs in traditional NER. However, the previous methods are not efficient for semi-supervised MNER. Because the MNER task is defined to combine the text information with image one and needs to consider the mismatch between the posted text and image. To fuse the text and image features for MNER effectively under semi-supervised setting, we propose a novel span-based multimodal variational autoencoder (SMVAE) model for semi-supervised MNER. The proposed method exploits modal-specific VAEs to model text and image latent features, and utilizes product-of-experts to acquire multimodal features. In our approach, the implicit relations between labels and multimodal features are modeled by multimodal VAE. Thus, the useful information of unlabeled data can be exploited in our method under semi-supervised setting. Experimental results on two benchmark datasets demonstrate that our approach not only outperforms baselines under supervised setting, but also improves MNER performance with less labeled data than existing semi-supervised methods.
R-TeaFor: Regularized Teacher-Forcing for Abstractive Summarization
Authors: Guan-Yu Lin, Pu-Jen Cheng
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Teacher-forcing is widely used in training sequence generation models to improve sampling efficiency and to stabilize training. However, teacher-forcing is vulnerable to the exposure bias problem. Previous works have attempted to address exposure bias by modifying the training data to simulate model-generated results. Nevertheless, they do not consider the pairwise relationship between the original training data and the modified ones, which provides more information during training. Hence, we propose Regularized Teacher-Forcing (R-TeaFor) to utilize this relationship for better regularization. Empirically, our experiments show that R-TeaFor outperforms previous summarization state-of-the-art models, and the results can be generalized to different pre-trained models.
Modeling Consistency Preference via Lexical Chains for Document-level Neural Machine Translation
Authors: Xinglin Lyu, Junhui Li, Shimin Tao, Hao Yang, Ying Qin, Min Zhang
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: In this paper we aim to relieve the issue of lexical translation inconsistency for document-level neural machine translation (NMT) by modeling consistency preference for lexical chains, which consist of repeated words in a source-side document and provide a representation of the lexical consistency structure of the document. Specifically, we first propose lexical-consistency attention to capture consistency context among words in the same lexical chains. Then for each lexical chain we define and learn a consistency-tailored latent variable, which will guide the translation of corresponding sentences to enhance lexical translation consistency. Experimental results on Chinese\textrightarrowEnglish and French\textrightarrowEnglish document-level translation tasks show that our approach not only significantly improves translation performance in BLEU, but also substantially alleviates the problem of the lexical translation inconsistency.
Just Fine-tune Twice: Selective Differential Privacy for Large Language Models
Authors: Weiyan Shi, Ryan Shea, Si Chen, Chiyuan Zhang, Ruoxi Jia, Zhou Yu
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Protecting large language models from privacy leakage is becoming increasingly crucial with their wide adoption in real-world products. Yet applying *differential privacy* (DP), a canonical notion with provable privacy guarantees for machine learning models, to those models remains challenging due to the trade-off between model utility and privacy loss. Utilizing the fact that sensitive information in language data tends to be sparse, Shi et al. (2021) formalized a DP notion extension called *Selective Differential Privacy* (SDP) to protect only the sensitive tokens defined by a policy function. However, their algorithm only works for RNN-based models. In this paper, we develop a novel framework, *Just Fine-tune Twice* (JFT), that achieves SDP for state-of-the-art large transformer-based models. Our method is easy to implement: it first fine-tunes the model with *redacted* in-domain data, and then fine-tunes it again with the *original* in-domain data using a private training mechanism. Furthermore, we study the scenario of imperfect implementation of policy functions that misses sensitive tokens and develop systematic methods to handle it. Experiments show that our method achieves strong utility compared to previous baselines. We also analyze the SDP privacy guarantee empirically with the canary insertion attack.
Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents
Authors: Marcio Fonseca, Yftah Ziser, Shay B. Cohen
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: We argue that disentangling content selection from the budget used to cover salient content improves the performance and applicability of abstractive summarizers. Our method, FactorSum, does this disentanglement by factorizing summarization into two steps through an energy function: (1) generation of abstractive summary views covering salient information in subsets of the input document (document views); (2) combination of these views into a final summary, following a budget and content guidance. This guidance may come from different sources, including from an advisor model such as BART or BigBird, or in oracle mode -- from the reference. This factorization achieves significantly higher ROUGE scores on multiple benchmarks for long document summarization, namely PubMed, arXiv, and GovReport. Most notably, our model is effective for domain adaptation. When trained only on PubMed samples, it achieves a 46.29 ROUGE-1 score on arXiv, outperforming PEGASUS trained in domain by a large margin. Our experimental results indicate that the performance gains are due to more flexible budget adaptation and processing of shorter contexts provided by partial document views.
Open-Domain Sign Language Translation Learned from Online Video
Authors: Bowen Shi, Diane Brentari, Gregory Shakhnarovich, Karen Livescu
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Existing work on sign language translation -- that is, translation from sign language videos into sentences in a written language -- has focused mainly on (1) data collected in a controlled environment or (2) data in a specific domain, which limits the applicability to real-world settings. In this paper, we introduce OpenASL, a large-scale American Sign Language (ASL) - English dataset collected from online video sites (e.g., YouTube).OpenASL contains 288 hours of ASL videos in multiple domains from over 200 signers and is the largest publicly available ASL translation dataset to date. To tackle the challenges of sign language translation in realistic settings and without glosses, we propose a set of techniques including sign search as a pretext task for pre-training and fusion of mouthing and handshape features. The proposed techniques produce consistent and large improvements in translation quality, over baseline models basedon prior work.
Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change
Authors: Zhaochen Su, Zecheng Tang, Xinyan Guan, Lijun Wu, Min Zhang, Juntao Li
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Recent research has revealed that neural language models at scale suffer from poor temporal generalization capability, i.e., language model pre-trained on static data from past years performs worse over time on emerging data. Existing methods mainly perform continual training to mitigate such a misalignment. While effective to some extent but is far from being addressed on both the language modeling and downstream tasks. In this paper, we empirically observe that temporal generalization is closely affiliated with lexical semantic change, which is one of the essential phenomena of natural languages. Based on this observation, we propose a simple yet effective lexical-level masking strategy to post-train a converged language model. Experiments on two pre-trained language models, two different classification tasks, and four benchmark datasets demonstrate the effectiveness of our proposed method over existing temporal adaptation methods, i.e., continual training with new data. Our code is available at https://github.com/zhaochen0110/LMLM.
ULN: Towards Underspecified Vision-and-Language Navigation
Authors: Weixi Feng, Tsu-Jui Fu, Yujie Lu, William Yang Wang
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Vision-and-Language Navigation (VLN) is a task to guide an embodied agent moving to a target position using language instructions. Despite the significant performance improvement, the wide use of fine-grained instructions fails to characterize more practical linguistic variations in reality. To fill in this gap, we introduce a new setting, namely Underspecified vision-and-Language Navigation (ULN), and associated evaluation datasets. ULN evaluates agents using multi-level underspecified instructions instead of purely fine-grained or coarse-grained, which is a more realistic and general setting. As a primary step toward ULN, we propose a VLN framework that consists of a classification module, a navigation agent, and an Exploitation-to-Exploration (E2E) module. Specifically, we propose to learn Granularity Specific Sub-networks (GSS) for the agent to ground multi-level instructions with minimal additional parameters. Then, our E2E module estimates grounding uncertainty and conducts multi-step lookahead exploration to improve the success rate further. Experimental results show that existing VLN models are still brittle to multi-level language underspecification. Our framework is more robust and outperforms the baselines on ULN by ~10% relative success rate across all levels.
Federated Model Decomposition with Private Vocabulary for Text Classification
Authors: Zhuo Zhang, Xiangjing Hu, Lizhen Qu, Qifan Wang, Zenglin Xu
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: With the necessity of privacy protection, it becomes increasingly vital to train deep neural models in a federated learning manner for natural language processing (NLP) tasks. However, recent studies show eavesdroppers (i.e., dishonest servers) can still reconstruct the private input in federated learning (FL). Such a data reconstruction attack relies on the mappings between vocabulary and associated word embedding in NLP tasks, which are unfortunately less studied in current FL methods. In this paper, we propose a fedrated model decomposition method that protects the privacy of vocabularies, shorted as FEDEVOCAB. In FEDEVOCAB, each participant keeps the local embedding layer in the local device and detaches the local embedding parameters from federated aggregation. However, it is challenging to train an accurate NLP model when the private mappings are unknown and vary across participants in a cross-device FL setting. To address this problem, we further propose an adaptive updating technique to improve the performance of local models. Experimental results show that FEDEVOCAB maintains competitive performance and provides better privacy-preserving capacity compared to status quo methods.
ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks
Authors: Kai Xiong, Xiao Ding, Zhongyang Li, Li Du, Ting Liu, Bing Qin, Yi Zheng, Baoxing Huai
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Causal chain reasoning (CCR) is an essential ability for many decision-making AI systems, which requires the model to build reliable causal chains by connecting causal pairs. However, CCR suffers from two main transitive problems: threshold effect and scene drift. In other words, the causal pairs to be spliced may have a conflicting threshold boundary or scenario.To address these issues, we propose a novel Reliable Causal chain reasoning framework (ReCo), which introduces exogenous variables to represent the threshold and scene factors of each causal pair within the causal chain, and estimates the threshold and scene contradictions across exogenous variables via structural causal recurrent neural networks (SRNN). Experiments show that ReCo outperforms a series of strong baselines on both Chinese and English CCR datasets. Moreover, by injecting reliable causal chain knowledge distilled by ReCo, BERT can achieve better performances on four downstream causal-related tasks than BERT models enhanced by other kinds of knowledge.
Video Question Answering: Datasets, Algorithms and Challenges
Authors: Yaoyao Zhong, Wei Ji, Junbin Xiao, Yicong Li, Weihong Deng, Tat-Seng Chua
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: This survey aims to sort out the recent advances in video question answering (VideoQA) and point towards future directions. We firstly categorize the datasets into 1) normal VideoQA, multi-modal VideoQA and knowledge-based VideoQA, according to the modalities invoked in the question-answer pairs, or 2) factoid VideoQA and inference VideoQA, according to the technical challenges in comprehending the questions and deriving the correct answers. We then summarize the VideoQA techniques, including those mainly designed for Factoid QA (e.g., the early spatio-temporal attention-based methods and the recently Transformer-based ones) and those targeted at explicit relation and logic inference (e.g., neural modular networks, neural symbolic methods, and graph-structured methods). Aside from the backbone techniques, we delve into the specific models and find out some common and useful insights either for video modeling, question answering, or for cross-modal correspondence learning. Finally, we point out the research trend of studying beyond factoid VideoQA to inference VideoQA, as well as towards the robustness and interpretability. Additionally, we maintain a repository, https://github.com/VRU-NExT/VideoQA, to keep trace of the latest VideoQA papers, datasets, and their open-source implementations if available. With these efforts, we strongly hope this survey could shed light on the follow-up VideoQA research.
Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation
Authors: Deng Cai, Xin Li, Jackie Chun-Sing Ho, Lidong Bing, Wai Lam
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: We introduce a new method to improve existing multilingual sentence embeddings with Abstract Meaning Representation (AMR). Compared with the original textual input, AMR is a structured semantic representation that presents the core concepts and relations in a sentence explicitly and unambiguously. It also helps reduce the surface variations across different expressions and languages. Unlike most prior work that only evaluates the ability to measure semantic similarity, we present a thorough evaluation of existing multilingual sentence embeddings and our improved versions, which include a collection of five transfer tasks in different downstream applications. Experiment results show that retrofitting multilingual sentence embeddings with AMR leads to better state-of-the-art performance on both semantic textual similarity and transfer tasks.
Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling
Authors: Zhijun Wang, Xuebo Liu, Min Zhang
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Existing research generally treats Chinese character as a minimum unit for representation. However, such Chinese character representation will suffer two bottlenecks: 1) Learning bottleneck, the learning cannot benefit from its rich internal features (e.g., radicals and strokes); and 2) Parameter bottleneck, each individual character has to be represented by a unique vector. In this paper, we introduce a novel representation method for Chinese characters to break the bottlenecks, namely StrokeNet, which represents a Chinese character by a Latinized stroke sequence (e.g., “凹 (concave)” to “ajaie” and “凸 (convex)” to “aeaqe”). Specifically, StrokeNet maps each stroke to a specific Latin character, thus allowing similar Chinese characters to have similar Latin representations. With the introduction of StrokeNet to neural machine translation (NMT), many powerful but not applicable techniques to non-Latin languages (e.g., shared subword vocabulary learning and ciphertext-based data augmentation) can now be perfectly implemented. Experiments on the widely-used NIST Chinese-English, WMT17 Chinese-English and IWSLT17 Japanese-English NMT tasks show that StrokeNet can provide a significant performance boost over the strong baselines with fewer model parameters, achieving 26.5 BLEU on the WMT17 Chinese-English task which is better than any previously reported results without using monolingual data. Code and scripts are freely available at https://github.com/zjwang21/StrokeNet.
Boundary-Driven Table-Filling for Aspect Sentiment Triplet Extraction
Authors: Yice Zhang, Yifan Yang, Yihui Li, Bin Liang, Shiwei Chen, Yixue Dang, Min Yang, Ruifeng Xu
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Aspect Sentiment Triplet Extraction (ASTE) aims to extract the aspect terms along with the corresponding opinion terms and the expressed sentiments in the review, which is an important task in sentiment analysis. Previous research efforts generally address the ASTE task in an end-to-end fashion through the table-filling formalization, in which the triplets are represented by a two-dimensional (2D) table of word-pair relations. Under this formalization, a term-level relation is decomposed into multiple independent word-level relations, which leads to relation inconsistency and boundary insensitivity in the face of multi-word aspect terms and opinion terms. To overcome these issues, we propose Boundary-Driven Table-Filling (BDTF), which represents each triplet as a relation region in the 2D table and transforms the ASTE task into detection and classification of relation regions. We also notice that the quality of the table representation greatly affects the performance of BDTF. Therefore, we develop an effective relation representation learning approach to learn the table representation, which can fully exploit both word-to-word interactions and relation-to-relation interactions. Experiments on several public benchmarks show that the proposed approach achieves state-of-the-art performances.
Attention and Edge-Label Guided Graph Convolutional Networks for Named Entity Recognition
Authors: Renjie Zhou, Zhongyi Xie, Jian Wan, Jilin Zhang, Yong Liao, Qiang Liu
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: It has been shown that named entity recognition (NER) could benefit from incorporating the long-distance structured information captured by dependency trees. However, dependency trees built by tools usually have a certain percentage of errors. Under such circumstances, how to better use relevant structured information while ignoring irrelevant or wrong structured information from the dependency trees to improve NER performance is still a challenging research problem. In this paper, we propose the Attention and Edge-Label guided Graph Convolution Network (AELGCN) model. Then, we integrate it into BiLSTM-CRF to form BiLSTM-AELGCN-CRF model. We design an edge-aware node joint update module and introduce a node-aware edge update module to explore hidden in structured information entirely and solve the wrong dependency label information to some extent. After two modules, we apply attention-guided GCN, which automatically learns how to attend to the relevant structured information selectively. We conduct extensive experiments on several standard datasets across four languages and achieve better results than previous approaches. Through experimental analysis, it is found that our proposed model can better exploit the structured information on the dependency tree to improve the recognition of long entities.
Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset
Authors: Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Event extraction (EE) is crucial to downstream tasks such as new aggregation and event knowledge graph construction. Most existing EE datasets manually define fixed event types and design specific schema for each of them, failing to cover diverse events emerging from the online text. Moreover, news titles, an important source of event mentions, have not gained enough attention in current EE research. In this paper, we present Title2Event, a large-scale sentence-level dataset benchmarking Open Event Extraction without restricting event types. Title2Event contains more than 42,000 news titles in 34 topics collected from Chinese web pages. To the best of our knowledge, it is currently the largest manually annotated Chinese dataset for open event extraction. We further conduct experiments on Title2Event with different models and show that the characteristics of titles make it challenging for event extraction, addressing the significance of advanced study on this problem. The dataset and baseline codes are available at https://open-event-hub.github.io/title2event.
Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and Models
Authors: Chaitanya Malaviya, Sudeep Bhatia, Mark Yatskar
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Cognitive psychologists have documented that humans use cognitive heuristics, or mental shortcuts, to make quick decisions while expending less effort. While performing annotation work on crowdsourcing platforms, we hypothesize that such heuristic use among annotators cascades on to data quality and model robustness. In this work, we study cognitive heuristic use in the context of annotating multiple-choice reading comprehension datasets. We propose tracking annotator heuristic traces, where we tangibly measure low-effort annotation strategies that could indicate usage of various cognitive heuristics. We find evidence that annotators might be using multiple such heuristics, based on correlations with a battery of psychological tests. Importantly, heuristic use among annotators determines data quality along several dimensions: (1) known biased models, such as partial input models, more easily solve examples authoredby annotators that rate highly on heuristic use, (2) models trained on annotators scoring highly on heuristic use don`t generalize as well, and (3) heuristic-seeking annotators tend to create qualitatively less challenging examples. Our findings suggest that tracking heuristic usage among annotators can potentially help with collecting challenging datasets and diagnosing model biases.
Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts
Authors: Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, Ashish Sabharwal
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Question-answering datasets require a broad set of reasoning skills. We show how to use question decompositions to teach language models these broad reasoning skills in a robust fashion. Specifically, we use widely available QDMR representations to programmatically create hard-to-cheat synthetic contexts for real questions in six multi-step reasoning datasets. These contexts are carefully designed to avoid common reasoning shortcuts prevalent in real contexts that prevent models from learning the right skills. This results in a pretraining dataset, named TeaBReaC, containing 525K multi-step questions (with associated formal programs) covering about 900 reasoning patterns. We show that pretraining standard language models (LMs) on TeaBReaC before fine-tuning them on target datasets improves their performance by up to 13 F1 points across 4 multi-step QA datasets, with up to 21 point gain on more complex questions. The resulting models also demonstrate higher robustness, with a 5-8 F1 point improvement on two contrast sets. Furthermore, TeaBReaC pretraining substantially improves model performance and robustness even when starting with numerate LMs pretrained using recent methods (e.g., PReasM, POET). Our work thus shows how to effectively use decomposition-guided contexts to robustly teach multi-step reasoning.
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation
Authors: Fan Yin, Yao Li, Cho-Jui Hsieh, Kai-Wei Chang
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Adversarial Examples Detection (AED) is a crucial defense technique against adversarial attacks and has drawn increasing attention from the Natural Language Processing (NLP) community. Despite the surge of new AED methods, our studies show that existing methods heavily rely on a shortcut to achieve good performance. In other words, current search-based adversarial attacks in NLP stop once model predictions change, and thus most adversarial examples generated by those attacks are located near model decision boundaries. To surpass this shortcut and fairly evaluate AED methods, we propose to test AED methods with \textbfFar \textbfBoundary (\textbfFB) adversarial examples. Existing methods show worse than random guess performance under this scenario. To overcome this limitation, we propose a new technique, \textbfADDMU, \textbfadversary \textbfdetection with \textbfdata and \textbfmodel \textbfuncertainty, which combines two types of uncertainty estimation for both regular and FB adversarial example detection. Our new method outperforms previous methods by 3.6 and 6.0 \textitAUC points under each scenario. Finally, our analysis shows that the two types of uncertainty provided by \textbfADDMU can be leveraged to characterize adversarialexamples and identify the ones that contribute most to model`s robustness in adversarial training.
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks
Authors: Zhongwei Wan, Yichun Yin, Wei Zhang, Jiaxin Shi, Lifeng Shang, Guangyong Chen, Xin Jiang, Qun Liu
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: General pre-trained language models (PLMs), such as BERT, have achieved remarkable performance on various NLP tasks. Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e.g., biomedical and computer science) by continuing to pre-train general PLMs with domain-specific corpora. However, this domain-adaptive pre-training (DAPT (CITATION)) tends to forget the previous general knowledge acquired by general PLMs, which leads to a \textitcatastrophic forgetting phenomenon and sub-optimal performance. To alleviate this problem, we propose a new framework of \textbfMemory-\textbfAugmented \textbfPre-trained Language Model (\textbfMAP), which augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge. Specifically, we propose a new memory-augmented layer, and based on it, different augmentation strategies are explored to build memory and fusion memory into domain-specific PLM. We demonstrate the effectiveness of MAP on different domains (biomedical and computer science publications, news, and reviews) and different kinds (text classification, QA, NER) of tasks, and the extensive results show that the proposed MAP can achieve SOTA results on these tasks.
Towards Unifying Reference Expression Generation and Comprehension
Authors: Duo Zheng, Tao Kong, Ya Jing, Jiaan Wang, Xiaojie Wang
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Reference Expression Generation (REG) and Comprehension (REC) are two highly correlated tasks. Modeling REG and REC simultaneously for utilizing the relation between them is a promising way to improve both. However, the problem of distinct inputs, as well as building connections between them in a single model, brings challenges to the design and training of the joint model. To address the problems, we propose a unified model for REG and REC, named UniRef. It unifies these two tasks with the carefully-designed Image-Region-Text Fusion layer (IRTF), which fuses the image, region and text via the image cross-attention and region cross-attention. Additionally, IRTF could generate pseudo input regions for the REC task to enable a uniform way for sharing the identical representation space across the REC and REG. We further propose Vision-conditioned Masked Language Modeling (VMLM) and Text-Conditioned Region Prediction (TRP) to pre-train UniRef model on multi-granular corpora. The VMLM and TRP are directly related to REG and REC, respectively, but could help each other. We conduct extensive experiments on three benchmark datasets, RefCOCO, RefCOCO+ and RefCOCOg. Experimental results show that our model outperforms previous state-of-the-art methods on both REG and REC.
Textual Manifold-based Defense Against Natural Language Adversarial Examples
Authors: Dang Nguyen Minh, Anh Tuan Luu
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Despite the recent success of large pretrained language models in NLP, they are susceptible to adversarial examples. Concurrently, several studies on adversarial images have observed an intriguing property: the adversarial images tend to leave the low-dimensional natural data manifold. In this study, we find a similar phenomenon occurs in the contextualized embedding space of natural sentences induced by pretrained language models in which textual adversarial examples tend to have their embeddings diverge off the manifold of natural sentence embeddings. Based on this finding, we propose Textual Manifold-based Defense (TMD), a defense mechanism that learns the embedding space manifold of the underlying language model and projects novel inputs back to the approximated structure before classification. Through extensive experiments, we find that our method consistently and significantly outperforms previous defenses under various attack settings while remaining unaffected to the clean accuracy. To the best of our knowledge, this is the first kind of manifold-based defense adapted to the NLP domain.
Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters
Authors: Hongyu Zhao, Hao Tan, Hongyuan Mei
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Adapter-tuning is a paradigm that transfers a pretrained language model to downstream tasks by adding and tuning a small number of new parameters. Previously proposed adapter architectures are all feed-forward neural networks. In this paper, we investigate the effectiveness of using tiny-attention---i.e., attention with extremely small per-head dimensionality---as adapters. Our tiny-attention adapter learns to modify the hidden states at each position directly conditioned on the hidden states at all the other positions, which is missed by the previously proposed adapters. Moreover, we view its multiple attention heads as a mixture of experts and propose to average their weights during deployment, which further reduces its inference computation cost. On the GLUE benchmark, our tiny-attention adapter outperforms the other parameter-efficient transfer learning methods as well as full fine-tuning while only updating 0.05% of the parameters. On the FewGLUE benchmark, its performance is comparable to that of GPT-3 and PET.
Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives
Authors: Si Sun, Chenyan Xiong, Yue Yu, Arnold Overwijk, Zhiyuan Liu, Jie Bao
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: In this paper, we investigate the instability in the standard dense retrieval training, which iterates between model training and hard negative selection using the being-trained model. We show the catastrophic forgetting phenomena behind the training instability, where models learn and forget different negative groups during training iterations. We then propose ANCE-Tele, which accumulates momentum negatives from past iterations and approximates future iterations using lookahead negatives, as “teleportations” along the time axis to smooth the learning process. On web search and OpenQA, ANCE-Tele outperforms previous state-of-the-art systems of similar size, eliminates the dependency on sparse retrieval negatives, and is competitive among systems using significantly more (50x) parameters. Our analysis demonstrates that teleportation negatives reduce catastrophic forgetting and improve convergence speed for dense retrieval training. The source code of this paper is available at https://github.com/OpenMatch/ANCE-Tele.
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts
Authors: Akari Asai, Mohammadreza Salehi, Matthew Peters, Hannaneh Hajishirzi
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: This work introduces a new multi-task, parameter-efficient language model (LM) tuning method that learns to transfer knowledge across different tasks via a mixture of soft prompts---small prefix embedding vectors pre-trained for different tasks. Our method, called ATTEMPT (ATTEntional Mixtures of Prompt Tuning), obtains source prompts as encodings of large-scale source tasks into a small number of parameters and trains an attention module to interpolate the source prompts and a newly initialized target prompt for every instance in the target task. During training, only the target task prompt and the attention weights, which are shared between tasks in multi-task training, are updated, while the original LM and source prompts are intact. ATTEMPT is highly parameter-efficient (e.g., updates 2,300 times fewer parameters than full fine-tuning), while it overcomes instability of prompt tuning and achieves high task performance using learned knowledge from high-resource tasks. Moreover, it is modular using pre-trained soft prompts, and can flexibly add or remove source prompts for effective knowledge transfer. Our experimental results across 21 diverse NLP datasets show that ATTEMPT significantly outperforms prompt tuning and outperforms or matches fully fine-tuned or other parameter-efficient tuning approaches that use 10 times more parameters. Finally, ATTEMPT outperforms previous work in few-shot learning settings.
Exploration of the Usage of Color Terms by Color-blind Participants in Online Discussion Platforms
Authors: Ella Rabinovich, Boaz Carmeli
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: Prominent questions about the role of sensory vs. linguistic input in the way we acquire and use language have been extensively studied in the psycholinguistic literature. However, the relative effect of various factors in a person`s overall experience on their linguistic system remains unclear. We study this question by making a step forward towards a better understanding of the conceptual perception of colors by color-blind individuals, as reflected in their spontaneous linguistic productions. Using a novel and carefully curated dataset, we show that red-green color-blind speakers use the “red” and “green” color terms in less predictable contexts, and in linguistic environments evoking mental image to a lower extent, when compared to their normal-sighted counterparts. These findings shed some new and interesting light on the role of sensory experience on our linguistic system.
DEER: Descriptive Knowledge Graph for Explaining Entity Relationships
Authors: Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang, Jinjun Xiong, Wen-mei Hwu
Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2022

Abstract: We propose DEER (Descriptive Knowledge Graph for Explaining Entity Relationships) - an open and informative form of modeling entity relationships. In DEER, relationships between entities are represented by free-text relation descriptions. For instance, the relationship between entities of machine learning and algorithm can be represented as “Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.” To construct DEER, we propose a self-supervised learning method to extract relation descriptions with the analysis of dependency patterns and generate relation descriptions with a transformer-based relation description synthesizing model, where no human labeling is required. Experiments demonstrate that our system can extract and generate high-quality relation descriptions for explaining entity relationships. The results suggest that we can build an open and informative knowledge graph without human annotation.