mtybilly/Instruct2DS huggingface dataset:
Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling
Authors: Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Transformer is important for text modeling. However, it has difficulty in handling long documents due to the quadratic complexity with input text length. In order to handle this problem, we propose a hierarchical interactive Transformer (Hi-Transformer) for efficient and effective long document modeling. Hi-Transformer models documents in a hierarchical way, i.e., first learns sentence representations and then learns document representations. It can effectively reduce the complexity and meanwhile capture global document context in the modeling of each sentence. More specifically, we first use a sentence Transformer to learn the representations of each sentence. Then we use a document Transformer to model the global document context from these sentence representations. Next, we use another sentence Transformer to enhance sentence modeling using the global document context. Finally, we use hierarchical pooling method to obtain document embedding. Extensive experiments on three benchmark datasets validate the efficiency and effectiveness of Hi-Transformer in long document modeling.
Robust Transfer Learning with Pretrained Language Models through Adapters
Authors: Wenjuan Han, Bo Pang, Ying Nian Wu
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Transfer learning with large pretrained transformer-based language models like BERT has become a dominating approach for most NLP tasks. Simply fine-tuning those large language models on downstream tasks or combining it with task-specific pretraining is often not robust. In particular, the performance considerably varies as the random seed changes or the number of pretraining and/or fine-tuning iterations varies, and the fine-tuned model is vulnerable to adversarial attack. We propose a simple yet effective adapter-based approach to mitigate these issues. Specifically, we insert small bottleneck layers (i.e., adapter) within each layer of a pretrained model, then fix the pretrained layers and train the adapter layers on the downstream task data, with (1) task-specific unsupervised pretraining and then (2) task-specific supervised training (e.g., classification, sequence labeling). Our experiments demonstrate that such a training scheme leads to improved stability and adversarial robustness in transfer learning to various downstream tasks.
Embracing Ambiguity: Shifting the Training Target of NLI Models
Authors: Johannes Mario Meissner, Napat Thumwanit, Saku Sugawara, Akiko Aizawa
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels. While many research works do not pay much attention to this fact, several recent efforts have been made to acknowledge and embrace the existence of ambiguity, such as UNLI and ChaosNLI. In this paper, we explore the option of training directly on the estimated label distribution of the annotators in the NLI task, using a learning loss based on this ambiguity distribution instead of the gold-labels. We prepare AmbiNLI, a trial dataset obtained from readily available sources, and show it is possible to reduce ChaosNLI divergence scores when finetuning on this data, a promising first step towards learning how to capture linguistic ambiguity. Additionally, we show that training on the same amount of data but targeting the ambiguity distribution instead of gold-labels can result in models that achieve higher performance and learn better representations for downstream tasks.
Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning
Authors: Zhiyuan Zeng, Keqing He, Yuanmeng Yan, Zijun Liu, Yanan Wu, Hong Xu, Huixing Jiang, Weiran Xu
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system. A key challenge of OOD detection is to learn discriminative semantic features. Traditional cross-entropy loss only focuses on whether a sample is correctly classified, and does not explicitly distinguish the margins between categories. In this paper, we propose a supervised contrastive learning objective to minimize intra-class variance by pulling together in-domain intents belonging to the same class and maximize inter-class variance by pushing apart samples from different classes. Besides, we employ an adversarial augmentation mechanism to obtain pseudo diverse views of a sample in the latent space. Experiments on two public datasets prove the effectiveness of our method capturing discriminative representations for OOD detection.
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking
Authors: Yinpei Dai, Hangyu Li, Yongbin Li, Jian Sun, Fei Huang, Luo Si, Xiaodan Zhu
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset. In this paper, we propose to use curriculum learning (CL) to better leverage both the curriculum structure and schema structure for task-oriented dialogs. Specifically, we propose a model-agnostic framework called Schema-aware Curriculum Learning for Dialog State Tracking (SaCLog), which consists of a preview module that pre-trains a DST model with schema information, a curriculum module that optimizes the model with CL, and a review module that augments mispredicted data to reinforce the CL training. We show that our proposed approach improves DST performance over both a transformer-based and RNN-based DST model (TripPy and TRADE) and achieves new state-of-the-art results on WOZ2.0 and MultiWOZ2.1.
On the Generation of Medical Dialogs for COVID-19
Authors: Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Under the pandemic of COVID-19, people experiencing COVID19-related symptoms have a pressing need to consult doctors. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialog system that can provide COVID19-related consultations. We collected two dialog datasets -- CovidDialog -- (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. While the largest of their kind, these two datasets are still relatively small compared with general-domain dialog datasets. Training complex dialog generation models on small datasets bears high risk of overfitting. To alleviate overfitting, we develop a multi-task learning approach, which regularizes the data-deficient dialog generation task with a masked token prediction task. Experiments on the CovidDialog datasets demonstrate the effectiveness of our approach. We perform both human evaluation and automatic evaluation of dialogs generated by our method. Results show that the generated responses are promising in being doctor-like, relevant to conversation history, clinically informative and correct. The code and the data are available at r̆lhttps://github.com/UCSD-AI4H/COVID-Dialogue.
Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images
Authors: Nyoungwoo Lee, Suwon Shin, Jaegul Choo, Ho-Jin Choi, Sung-Hyon Myaeng
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: In multi-modal dialogue systems, it is important to allow the use of images as part of a multi-turn conversation. Training such dialogue systems generally requires a large-scale dataset consisting of multi-turn dialogues that involve images, but such datasets rarely exist. In response, this paper proposes a 45k multi-modal dialogue dataset created with minimal human intervention. Our method to create such a dataset consists of (1) preparing and pre-processing text dialogue datasets, (2) creating image-mixed dialogues by using a text-to-image replacement technique, and (3) employing a contextual-similarity-based filtering step to ensure the contextual coherence of the dataset. To evaluate the validity of our dataset, we devise a simple retrieval model for dialogue sentence prediction tasks. Automatic metrics and human evaluation results on such tasks show that our dataset can be effectively used as training data for multi-modal dialogue systems which require an understanding of images and text in a context-aware manner. Our dataset and generation code is available at r̆lhttps://github.com/shh1574/multi-modal-dialogue-dataset.
BERTTune: Fine-Tuning Neural Machine Translation with BERTScore
Authors: Inigo Jauregi Unanue, Jacob Parnell, Massimo Piccardi
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Neural machine translation models are often biased toward the limited translation references seen during training. To amend this form of overfitting, in this paper we propose fine-tuning the models with a novel training objective based on the recently-proposed BERTScore evaluation metric. BERTScore is a scoring function based on contextual embeddings that overcomes the typical limitations of n-gram-based metrics (e.g. synonyms, paraphrases), allowing translations that are different from the references, yet close in the contextual embedding space, to be treated as substantially correct. To be able to use BERTScore as a training objective, we propose three approaches for generating soft predictions, allowing the network to remain completely differentiable end-to-end. Experiments carried out over four, diverse language pairs show improvements of up to 0.58 pp (3.28%) in BLEU score and up to 0.76 pp (0.98%) in BERTScore (F_BERT) when fine-tuning a strong baseline.
Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain
Authors: Wei Shi, Vera Demberg
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Implicit discourse relation classification is a challenging task, in particular when the text domain is different from the standard Penn Discourse Treebank (PDTB; Prasad et al., 2008) training corpus domain (Wall Street Journal in 1990s). We here tackle the task of implicit discourse relation classification on the biomedical domain, for which the Biomedical Discourse Relation Bank (BioDRB; Prasad et al., 2011) is available. We show that entity information can be used to improve discourse relational argument representation. In a first step, we show that explicitly marked instances that are content-wise similar to the target relations can be used to achieve good performance in the cross-domain setting using a simple unsupervised voting pipeline. As a further step, we show that with the linked entity information from the first step, a transformer which is augmented with entity-related information (KBERT; Liu et al., 2020) sets the new state of the art performance on the dataset, outperforming the large pre-trained BioBERT (Lee et al., 2020) model by 2% points.
Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction
Authors: Ming Shen, Pratyay Banerjee, Chitta Baral
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: In this work, we propose Masked Noun-Phrase Prediction (MNPP), a pre-training strategy to tackle pronoun resolution in a fully unsupervised setting. Firstly, We evaluate our pre-trained model on various pronoun resolution datasets without any finetuning. Our method outperforms all previous unsupervised methods on all datasets by large margins. Secondly, we proceed to a few-shot setting where we finetune our pre-trained model on WinoGrande-S and XS separately. Our method outperforms RoBERTa-large baseline with large margins, meanwhile, achieving a higher AUC score after further finetuning on the remaining three official splits of WinoGrande.
Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction
Authors: Chenliang Li, Bin Bi, Ming Yan, Wei Wang, Songfang Huang
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Recently, question answering (QA) based on machine reading comprehension has become popular. This work focuses on generative QA which aims to generate an abstractive answer to a given question instead of extracting an answer span from a provided passage. Generative QA often suffers from two critical problems: (1) summarizing content irrelevant to a given question, (2) drifting away from a correct answer during generation. In this paper, we address these problems by a novel Rationale-Enriched Answer Generator (REAG), which incorporates an extractive mechanism into a generative model. Specifically, we add an extraction task on the encoder to obtain the rationale for an answer, which is the most relevant piece of text in an input document to a given question. Based on the extracted rationale and original input, the decoder is expected to generate an answer with high confidence. We jointly train REAG on the MS MARCO QA+NLG task and the experimental results show that REAG improves the quality and semantic accuracy of answers over baseline models.
Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning
Authors: Linzi Xing, Wen Xiao, Giuseppe Carenini
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: In news articles the lead bias is a common phenomenon that usually dominates the learning signals for neural extractive summarizers, severely limiting their performance on data with different or even no bias. In this paper, we introduce a novel technique to demote lead bias and make the summarizer focus more on the content semantics. Experiments on two news corpora with different degrees of lead bias show that our method can effectively demote the model`s learned lead bias and improve its generality on out-of-distribution data, with little to no performance loss on in-distribution data.
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications
Authors: Hongxuan Tang, Hongyu Li, Jing Liu, Yu Hong, Hua Wu, Haifeng Wang
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Machine reading comprehension (MRC) is a crucial task in natural language processing and has achieved remarkable advancements. However, most of the neural MRC models are still far from robust and fail to generalize well in real-world applications. In order to comprehensively verify the robustness and generalization of MRC models, we introduce a real-world Chinese dataset -- DuReader_robust . It is designed to evaluate the MRC models from three aspects: over-sensitivity, over-stability and generalization. Comparing to previous work, the instances in DuReader_robust are natural texts, rather than the altered unnatural texts. It presents the challenges when applying MRC models to real-world applications. The experimental results show that MRC models do not perform well on the challenge test set. Moreover, we analyze the behavior of existing models on the challenge test set, which may provide suggestions for future model development. The dataset and codes are publicly available at r̆lhttps://github.com/baidu/DuReader.
Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solving
Authors: Shih-hung Tsai, Chao-Chun Liang, Hsin-Min Wang, Keh-Yih Su
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: With the recent advancements in deep learning, neural solvers have gained promising results in solving math word problems. However, these SOTA solvers only generate binary expression trees that contain basic arithmetic operators and do not explicitly use the math formulas. As a result, the expression trees they produce are lengthy and uninterpretable because they need to use multiple operators and constants to represent one single formula. In this paper, we propose sequence-to-general tree (S2G) that learns to generate interpretable and executable operation trees where the nodes can be formulas with an arbitrary number of arguments. With nodes now allowed to be formulas, S2G can learn to incorporate mathematical domain knowledge into problem-solving, making the results more interpretable. Experiments show that S2G can achieve a better performance against strong baselines on problems that require domain knowledge.
Multi-Scale Progressive Attention Network for Video Question Answering
Authors: Zhicheng Guo, Jiaxuan Zhao, Licheng Jiao, Xu Liu, Lingling Li
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Understanding the multi-scale visual information in a video is essential for Video Question Answering (VideoQA). Therefore, we propose a novel Multi-Scale Progressive Attention Network (MSPAN) to achieve relational reasoning between cross-scale video information. We construct clips of different lengths to represent different scales of the video. Then, the clip-level features are aggregated into node features by using max-pool, and a graph is generated for each scale of clips. For cross-scale feature interaction, we design a message passing strategy between adjacent scale graphs, i.e., top-down scale interaction and bottom-up scale interaction. Under the question`s guidance of progressive attention, we realize the fusion of all-scale video features. Experimental evaluations on three benchmarks: TGIF-QA, MSVD-QA and MSRVTT-QA show our method has achieved state-of-the-art performance.
Efficient Passage Retrieval with Hashing for Open-domain Question Answering
Authors: Ikuya Yamada, Akari Asai, Hannaneh Hajishirzi
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Most state-of-the-art open-domain question answering systems use a neural retrieval model to encode passages into continuous vectors and extract them from a knowledge source. However, such retrieval models often require large memory to run because of the massive size of their passage index. In this paper, we introduce Binary Passage Retriever (BPR), a memory-efficient neural retrieval model that integrates a learning-to-hash technique into the state-of-the-art Dense Passage Retriever (DPR) to represent the passage index using compact binary codes rather than continuous vectors. BPR is trained with a multi-task objective over two tasks: efficient candidate generation based on binary codes and accurate reranking based on continuous vectors. Compared with DPR, BPR substantially reduces the memory cost from 65GB to 2GB without a loss of accuracy on two standard open-domain question answering benchmarks: Natural Questions and TriviaQA. Our code and trained models are available at r̆lhttps://github.com/studio-ousia/bpr.
Entity Concept-enhanced Few-shot Relation Extraction
Authors: Shan Yang, Yongfei Zhang, Guanglin Niu, Qinghua Zhao, Shiliang Pu
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Few-shot relation extraction (FSRE) is of great importance in long-tail distribution problem, especially in special domain with low-resource data. Most existing FSRE algorithms fail to accurately classify the relations merely based on the information of the sentences together with the recognized entity pairs, due to limited samples and lack of knowledge. To address this problem, in this paper, we proposed a novel entity CONCEPT-enhanced FEw-shot Relation Extraction scheme (ConceptFERE), which introduces the inherent concepts of entities to provide clues for relation prediction and boost the relations classification performance. Firstly, a concept-sentence attention module is developed to select the most appropriate concept from multiple concepts of each entity by calculating the semantic similarity between sentences and concepts. Secondly, a self-attention based fusion module is presented to bridge the gap of concept embedding and sentence embedding from different semantic spaces. Extensive experiments on the FSRE benchmark dataset FewRel have demonstrated the effectiveness and the superiority of the proposed ConceptFERE scheme as compared to the state-of-the-art baselines. Code is available at r̆lhttps://github.com/LittleGuoKe/ConceptFERE.
Improving Model Generalization: A Chinese Named Entity Recognition Case Study
Authors: Guanqing Liang, Cane Wing-Ki Leung
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Generalization is an important ability that helps to ensure that a machine learning model can perform well on unseen data. In this paper, we study the effect of data bias on model generalization, using Chinese Named Entity Recognition (NER) as a case study. Specifically, we analyzed five benchmarking datasets for Chinese NER, and observed the following two types of data bias that can compromise model generalization ability. Firstly, the test sets of all the five datasets contain a significant proportion of entities that have been seen in the training sets. Such test data would therefore not be able to reflect the true generalization ability of a model. Secondly, all datasets are dominated by a few fat-head entities, i.e., entities appearing with particularly high frequency. As a result, a model might be able to produce high prediction accuracy simply by keyword memorization without leveraging context knowledge. To address these data biases, we first refine each test set by excluding seen entities from it, so as to better evaluate a model`s generalization ability. Then, we propose a simple yet effective entity resampling method to make entities within the same category distributed equally, encouraging a model to leverage both name and context knowledge in the training process. Experimental results demonstrate that the proposed entity resampling method significantly improves a model`s ability in detecting unseen entities, especially for company, organization and position categories.
Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction
Authors: Quzhe Huang, Shengqi Zhu, Yansong Feng, Yuan Ye, Yuxuan Lai, Dongyan Zhao
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Document-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences. Yet, human annotators usually use a small number of sentences to identify the relationship between a given entity pair. In this paper, we present an embarrassingly simple but effective method to heuristically select evidence sentences for document-level RE, which can be easily combined with BiLSTM to achieve good performance on benchmark datasets, even better than fancy graph neural network based methods. We have released our code at r̆lhttps://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need.
Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders
Authors: Irene Li, Vanessa Yan, Tianxiao Li, Rihao Qu, Dragomir Radev
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Learning prerequisite chains is an important task for one to pick up knowledge efficiently in both known and unknown domains. For example, one may be an expert in the natural language processing (NLP) domain, but want to determine the best order in which to learn new concepts in an unfamiliar Computer Vision domain (CV). Both domains share some common concepts, such as machine learning basics and deep learning models. In this paper, we solve the task of unsupervised cross-domain concept prerequisite chain learning, using an optimized variational graph autoencoder. Our model learns to transfer concept prerequisite relations from an information-rich domain (source domain) to an information-poor domain (target domain), substantially surpassing other baseline models. In addition, we expand an existing dataset by introducing two new domains----CV and Bioinformatics (BIO). The annotated data and resources as well as the code will be made publicly available.
Attentive Multiview Text Representation for Differential Diagnosis
Authors: Hadi Amiri, Mitra Mohtarami, Isaac Kohane
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: We present a text representation approach that can combine different views (representations) of the same input through effective data fusion and attention strategies for ranking purposes. We apply our model to the problem of differential diagnosis, which aims to find the most probable diseases that match with clinical descriptions of patients, using data from the Undiagnosed Diseases Network. Our model outperforms several ranking approaches (including a commercially-supported system) by effectively prioritizing and combining representations obtained from traditional and recent text representation techniques. We elaborate on several aspects of our model and shed light on its improved performance.
MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain
Authors: Christine Herlihy, Rachel Rudinger
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Crowdworker-constructed natural language inference (NLI) datasets have been found to contain statistical artifacts associated with the annotation process that allow hypothesis-only classifiers to achieve better-than-random performance (CITATION). We investigate whether MedNLI, a physician-annotated dataset with premises extracted from clinical notes, contains such artifacts (CITATION). We find that entailed hypotheses contain generic versions of specific concepts in the premise, as well as modifiers related to responsiveness, duration, and probability. Neutral hypotheses feature conditions and behaviors that co-occur with, or cause, the condition(s) in the premise. Contradiction hypotheses feature explicit negation of the premise and implicit negation via assertion of good health. Adversarial filtering demonstrates that performance degrades when evaluated on the \textitdifficult subset. We provide partition information and recommendations for alternative dataset construction strategies for knowledge-intensive domains.
Towards a more Robust Evaluation for Conversational Question Answering
Authors: Wissam Siblini, Baris Sayil, Yacine Kessaci
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: With the explosion of chatbot applications, Conversational Question Answering (CQA) has generated a lot of interest in recent years. Among proposals, reading comprehension models which take advantage of the conversation history (previous QA) seem to answer better than those which only consider the current question. Nevertheless, we note that the CQA evaluation protocol has a major limitation. In particular, models are allowed, at each turn of the conversation, to access the ground truth answers of the previous turns. Not only does this severely prevent their applications in fully autonomous chatbots, it also leads to unsuspected biases in their behavior. In this paper, we highlight this effect and propose new tools for evaluation and training in order to guard against the noted issues. The new results that we bring come to reinforce methods of the current state of the art.
VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension
Authors: Haoyang Wen, Anthony Ferritto, Heng Ji, Radu Florian, Avi Sil
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Existing models on Machine Reading Comprehension (MRC) require complex model architecture for effectively modeling long texts with paragraph representation and classification, thereby making inference computationally inefficient for production use. In this work, we propose VAULT: a light-weight and parallel-efficient paragraph representation for MRC based on contextualized representation from long document input, trained using a new Gaussian distribution-based objective that pays close attention to the partially correct instances that are close to the ground-truth. We validate our VAULT architecture showing experimental results on two benchmark MRC datasets that require long context modeling; one Wikipedia-based (Natural Questions (NQ)) and the other on TechNotes (TechQA). VAULT can achieve comparable performance on NQ with a state-of-the-art (SOTA) complex document modeling approach while being 16 times faster, demonstrating the efficiency of our proposed model. We also demonstrate that our model can also be effectively adapted to a completely different domain -- TechQA -- with large improvement over a model fine-tuned on a previously published large PLM.
Avoiding Overlap in Data Augmentation for AMR-to-Text Generation
Authors: Wenchao Du, Jeffrey Flanigan
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Leveraging additional unlabeled data to boost model performance is common practice in machine learning and natural language processing. For generation tasks, if there is overlap between the additional data and the target text evaluation data, then training on the additional data is training on answers of the test set. This leads to overly-inflated scores with the additional data compared to real-world testing scenarios and problems when comparing models. We study the AMR dataset and Gigaword, which is popularly used for improving AMR-to-text generators, and find significant overlap between Gigaword and a subset of the AMR dataset. We propose methods for excluding parts of Gigaword to remove this overlap, and show that our approach leads to a more realistic evaluation of the task of AMR-to-text generation. Going forward, we give simple best-practice recommendations for leveraging additional data in AMR-to-text generation.
Weakly-Supervised Methods for Suicide Risk Assessment: Role of Related Domains
Authors: Chenghao Yang, Yudong Zhang, Smaranda Muresan
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Social media has become a valuable resource for the study of suicidal ideation and the assessment of suicide risk. Among social media platforms, Reddit has emerged as the most promising one due to its anonymity and its focus on topic-based communities (subreddits) that can be indicative of someone`s state of mind or interest regarding mental health disorders such as r/SuicideWatch, r/Anxiety, r/depression. A challenge for previous work on suicide risk assessment has been the small amount of labeled data. We propose an empirical investigation into several classes of weakly-supervised approaches, and show that using pseudo-labeling based on related issues around mental health (e.g., anxiety, depression) helps improve model performance for suicide risk assessment.
Can Transformer Models Measure Coherence In Text: Re-Thinking the Shuffle Test
Authors: Philippe Laban, Luke Dai, Lucas Bandarkar, Marti A. Hearst
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: The Shuffle Test is the most common task to evaluate whether NLP models can measure coherence in text. Most recent work uses direct supervision on the task; we show that by simply finetuning a RoBERTa model, we can achieve a near perfect accuracy of 97.8%, a state-of-the-art. We argue that this outstanding performance is unlikely to lead to a good model of text coherence, and suggest that the Shuffle Test should be approached in a Zero-Shot setting: models should be evaluated without being trained on the task itself. We evaluate common models in this setting, such as Generative and Bi-directional Transformers, and find that larger architectures achieve high-performance out-of-the-box. Finally, we suggest the k-Block Shuffle Test, a modification of the original by increasing the size of blocks shuffled. Even though human reader performance remains high (around 95% accuracy), model performance drops from 94% to 78% as block size increases, creating a conceptually simple challenge to benchmark NLP models.
SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
Authors: Yixin Liu, Pengfei Liu
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: In this paper, we present a conceptually simple while empirically powerful framework for abstractive summarization, SimCLS, which can bridge the gap between the learning objective and evaluation metrics resulting from the currently dominated sequence-to-sequence learning framework by formulating text generation as a reference-free evaluation problem (i.e., quality estimation) assisted by contrastive learning. Experimental results show that, with minor modification over existing top-scoring systems, SimCLS can improve the performance of existing top-performing models by a large margin. Particularly, 2.51 absolute improvement against BART and 2.50 over PEGASUS w.r.t ROUGE-1 on the CNN/DailyMail dataset, driving the state-of-the-art performance to a new level. We have open-sourced our codes and results: r̆lhttps://github.com/yixinL7/SimCLS. Results of our proposed models have been deployed into ExplainaBoard platform, which allows researchers to understand our systems in a more fine-grained way.
SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles
Authors: Ana-Cristina Rogoz, Gaman Mihaela, Radu Tudor Ionescu
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: In this work, we introduce a corpus for satire detection in Romanian news. We gathered 55,608 public news articles from multiple real and satirical news sources, composing one of the largest corpora for satire detection regardless of language and the only one for the Romanian language. We provide an official split of the text samples, such that training news articles belong to different sources than test news articles, thus ensuring that models do not achieve high performance simply due to overfitting. We conduct experiments with two state-of-the-art deep neural models, resulting in a set of strong baselines for our novel corpus. Our results show that the machine-level accuracy for satire detection in Romanian is quite low (under 73% on the test set) compared to the human-level accuracy (87%), leaving enough room for improvement in future research.
Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents
Authors: Rui Meng, Khushboo Thaker, Lei Zhang, Yue Dong, Xingdi Yuan, Tong Wang, Daqing He
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Faceted summarization provides briefings of a document from different perspectives. Readers can quickly comprehend the main points of a long document with the help of a structured outline. However, little research has been conducted on this subject, partially due to the lack of large-scale faceted summarization datasets. In this study, we present FacetSum, a faceted summarization benchmark built on Emerald journal articles, covering a diverse range of domains. Different from traditional document-summary pairs, FacetSum provides multiple summaries, each targeted at specific sections of a long document, including the purpose, method, findings, and value. Analyses and empirical results on our dataset reveal the importance of bringing structure into summaries. We believe FacetSum will spur further advances in summarization research and foster the development of NLP systems that can leverage the structured information in both long texts and summaries.
Replicating and Extending “Because Their Treebanks Leak”: Graph Isomorphism, Covariants, and Parser Performance
Authors: Mark Anderson, Anders Søgaard, Carlos Gómez-Rodríguez
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: Søgaard (2020) obtained results suggesting the fraction of trees occurring in the test data isomorphic to trees in the training set accounts for a non-trivial variation in parser performance. Similar to other statistical analyses in NLP, the results were based on evaluating linear regressions. However, the study had methodological issues and was undertaken using a small sample size leading to unreliable results. We present a replication study in which we also bin sentences by length and find that only a small subset of sentences vary in performance with respect to graph isomorphism. Further, the correlation observed between parser performance and graph isomorphism in the wild disappears when controlling for covariants. However, in a controlled experiment, where covariants are kept fixed, we do observe a correlation. We suggest that conclusions drawn from statistical analyses like this need to be tempered and that controlled experiments can complement them by more readily teasing factors apart.
Don`t Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data
Authors: Rajat Bhatnagar, Ananya Ganesh, Katharina Kann
Published in: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Short Paper Track), 2021

Abstract: High-performing machine translation (MT) systems can help overcome language barriers while making it possible for everyone to communicate and use language technologies in the language of their choice. However, such systems require large amounts of parallel sentences for training, and translators can be difficult to find and expensive. Here, we present a data collection strategy for MT which, in contrast, is cheap and simple, as it does not require bilingual speakers. Based on the insight that humans pay specific attention to movements, we use graphics interchange formats (GIFs) as a pivot to collect parallel sentences from monolingual annotators. We use our strategy to collect data in Hindi, Tamil and English. As a baseline, we also collect data using images as a pivot. We perform an intrinsic evaluation by manually evaluating a subset of the sentence pairs and an extrinsic evaluation by finetuning mBART (Liu et al., 2020) on the collected data. We find that sentences collected via GIFs are indeed of higher quality.
Adversarial Multi-task Learning for Text Classification
Authors: Pengfei Liu, Xipeng Qiu, Xuanjing Huang
Published in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2017

Abstract: Neural network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant features. However, in most existing approaches, the extracted shared features are prone to be contaminated by task-specific features or the noise brought by other tasks. In this paper, we propose an adversarial multi-task learning framework, alleviating the shared and private latent feature spaces from interfering with each other. We conduct extensive experiments on 16 different text classification tasks, which demonstrates the benefits of our approach. Besides, we show that the shared knowledge learned by our proposed model can be regarded as off-the-shelf knowledge and easily transferred to new tasks. The datasets of all 16 tasks are publicly available at r̆lhttp://nlp.fudan.edu.cn/data/.
Neural End-to-End Learning for Computational Argumentation Mining
Authors: Steffen Eger, Johannes Daxenberger, Iryna Gurevych
Published in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2017

Abstract: We investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiLSTMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learning \textquoteleftnatural' subtasks, in a multi-task learning setup, improves performance.
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision
Authors: Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, Ni Lao
Published in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2017

Abstract: Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic Machine, which contains (a) a neural “programmer”, i.e., a sequence-to-sequence model that maps language utterances to programs and utilizes a key-variable memory to handle compositionality (b) a symbolic “computer”, i.e., a Lisp interpreter that performs program execution, and helps find good programs by pruning the search space. We apply REINFORCE to directly optimize the task reward of this structured prediction problem. To train with weak supervision and improve the stability of REINFORCE, we augment it with an iterative maximum-likelihood training process. NSM outperforms the state-of-the-art on the WebQuestionsSP dataset when trained from question-answer pairs only, without requiring any feature engineering or domain-specific knowledge.
Neural Relation Extraction with Multi-lingual Attention
Authors: Yankai Lin, Zhiyuan Liu, Maosong Sun
Published in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Long Paper Track), 2017

Abstract: Relation extraction has been widely used for finding unknown relational facts from plain text. Most existing methods focus on exploiting mono-lingual data for relation extraction, ignoring massive information from the texts in various languages. To address this issue, we introduce a multi-lingual neural relation extraction framework, which employs mono-lingual attention to utilize the information within mono-lingual texts and further proposes cross-lingual attention to consider the information consistency and complementarity among cross-lingual texts. Experimental results on real-world datasets show that, our model can take advantage of multi-lingual texts and consistently achieve significant improvements on relation extraction as compared with baselines.