mtybilly/Instruct2DS huggingface dataset:
Multi-granularity Textual Adversarial Attack with Behavior Cloning
Authors: Jin Su, Wei Wei Yangyi Chen
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Recently, the textual adversarial attack models become increasingly popular due to their successful in estimating the robustness of NLP models. However, existing works have obvious deficiencies. (1)They usually consider only a single granularity of modification strategies (e.g. word-level or sentence-level), which is insufficient to explore the holistic textual space for generation; (2) They need to query victim models hundreds of times to make a successful attack, which is highly inefficient in practice. To address such problems, in this paper we propose MAYA, a Multi-grAnularitY Attack model to effectively generate high-quality adversarial samples with fewer queries to victim models. Furthermore, we propose a reinforcement-learning based method to train a multi-granularity attack agent through behavior cloning with the expert knowledge from our MAYA algorithm to further reduce the query times. Additionally, we also adapt the agent to attack black-box models that only output labels without confidence scores. We conduct comprehensive experiments to evaluate our attack models by attacking BiLSTM, BERT and RoBERTa in two different black-box attack settings and three benchmark datasets. Experimental results show that our models achieve overall better attacking performance and produce more fluent and grammatical adversarial samples compared to baseline models. Besides, our adversarial attack agent significantly reduces the query times in both attack settings. Our codes are released at r̆lhttps://github.com/Yangyi-Chen/MAYA.
All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality
Authors: Marten van Schijndel William Timkey
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Similarity measures are a vital tool for understanding how language models represent and process language. Standard representational similarity measures such as cosine similarity and Euclidean distance have been successfully used in static word embedding models to understand how words cluster in semantic space. Recently, these measures have been applied to embeddings from contextualized models such as BERT and GPT-2. In this work, we call into question the informativity of such measures for contextualized language models. We find that a small number of rogue dimensions, often just 1-3, dominate these measures. Moreover, we find a striking mismatch between the dimensions that dominate similarity measures and those which are important to the behavior of the model. We show that simple postprocessing techniques such as standardization are able to correct for rogue dimensions and reveal underlying representational quality. We argue that accounting for rogue dimensions is essential for any similarity-based analysis of contextual language models.
Incorporating Residual and Normalization Layers into Analysis of Masked Language Models
Authors: Tatsuki Kuribayashi, Sho Yokoi, Kentaro Inui Goro Kobayashi
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Transformer architecture has become ubiquitous in the natural language processing field. To interpret the Transformer-based models, their attention patterns have been extensively analyzed. However, the Transformer architecture is not only composed of the multi-head attention; other components can also contribute to Transformers' progressive performance. In this study, we extended the scope of the analysis of Transformers from solely the attention patterns to the whole attention block, i.e., multi-head attention, residual connection, and layer normalization. Our analysis of Transformer-based masked language models shows that the token-to-token interaction performed via attention has less impact on the intermediate representations than previously assumed. These results provide new intuitive explanations of existing reports; for example, discarding the learned attention patterns tends not to adversely affect the performance. The codes of our experiments are publicly available.
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer
Authors: Yangyi Chen, Xurui Zhang, Mukai Li, Zhiyuan Liu, Maosong Sun Fanchao Qi
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Adversarial attacks and backdoor attacks are two common security threats that hang over deep learning. Both of them harness task-irrelevant features of data in their implementation. Text style is a feature that is naturally irrelevant to most NLP tasks, and thus suitable for adversarial and backdoor attacks. In this paper, we make the first attempt to conduct adversarial and backdoor attacks based on text style transfer, which is aimed at altering the style of a sentence while preserving its meaning. We design an adversarial attack method and a backdoor attack method, and conduct extensive experiments to evaluate them. Experimental results show that popular NLP models are vulnerable to both adversarial and backdoor attacks based on text style transfer---the attack success rates can exceed 90% without much effort. It reflects the limited ability of NLP models to handle the feature of text style that has not been widely realized. In addition, the style transfer-based adversarial and backdoor attack methods show superiority to baselines in many aspects. All the code and data of this paper can be obtained at r̆lhttps://github.com/thunlp/StyleAttack.
Sociolectal Analysis of Pretrained Language Models
Authors: Xin Zhang, Weiming Zhang, Anders Søgaard Sheng Zhang
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Using data from English cloze tests, in which subjects also self-reported their gender, age, education, and race, we examine performance differences of pretrained language models across demographic groups, defined by these (protected) attributes. We demonstrate wide performance gaps across demographic groups and show that pretrained language models systematically disfavor young non-white male speakers; i.e., not only do pretrained language models learn social biases (stereotypical associations) -- pretrained language models also learn sociolectal biases, learning to speak more like some than like others. We show, however, that, with the exception of BERT models, larger pretrained language models reduce some the performance gaps between majority and minority groups.
Examining Cross-lingual Contextual Embeddings with Orthogonal Structural Probes
Authors: David Mareček Tomasz Limisiewicz
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: State-of-the-art contextual embeddings are obtained from large language models available only for a few languages. For others, we need to learn representations using a multilingual model. There is an ongoing debate on whether multilingual embeddings can be aligned in a space shared across many languages. The novel Orthogonal Structural Probe (Limisiewicz and Mareček, 2021) allows us to answer this question for specific linguistic features and learn a projection based only on mono-lingual annotated datasets. We evaluate syntactic (UD) and lexical (WordNet) structural information encoded inmBERT`s contextual representations for nine diverse languages. We observe that for languages closely related to English, no transformation is needed. The evaluated information is encoded in a shared cross-lingual embedding space. For other languages, it is beneficial to apply orthogonal transformation learned separately for each language. We successfully apply our findings to zero-shot and few-shot cross-lingual parsing.
Are Transformers a Modern Version of ELIZA? Observations on French Object Verb Agreement
Authors: Guillaume Wisniewski, Benoit Crabbé Bingzhi Li
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Many recent works have demonstrated that unsupervised sentence representations of neural networks encode syntactic information by observing that neural language models are able to predict the agreement between a verb and its subject. We take a critical look at this line of research by showing that it is possible to achieve high accuracy on this agreement task with simple surface heuristics, indicating a possible flaw in our assessment of neural networks' syntactic ability. Our fine-grained analyses of results on the long-range French object-verb agreement show that contrary to LSTMs, Transformers are able to capture a non-trivial amount of grammatical structure.
Fine-grained Entity Typing via Label Reasoning
Authors: Hongyu Lin, Xinyan Xiao, Xianpei Han, Le Sun, Hua Wu Qing Liu
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types. In this paper, we argue that the implicitly entailed extrinsic and intrinsic dependencies between labels can provide critical knowledge to tackle the above challenges. To this end, we propose Label Reasoning Network(LRN), which sequentially reasons fine-grained entity labels by discovering and exploiting label dependencies knowledge entailed in the data. Specifically, LRN utilizes an auto-regressive network to conduct deductive reasoning and a bipartite attribute graph to conduct inductive reasoning between labels, which can effectively model, learn and reason complex label dependencies in a sequence-to-set, end-to-end manner. Experiments show that LRN achieves the state-of-the-art performance on standard ultra fine-grained entity typing benchmarks, and can also resolve the long tail label problem effectively.
Enhanced Language Representation with Label Knowledge for Span Extraction
Authors: Xin Cong, Zhenyu Sun, Xingwu Liu Pan Yang
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Span extraction, aiming to extract text spans (such as words or phrases) from plain text, is a fundamental process in Information Extraction. Recent works introduce the label knowledge to enhance the text representation by formalizing the span extraction task into a question answering problem (QA Formalization), which achieves state-of-the-art performance. However, such a QA Formalization does not fully exploit the label knowledge and causes a dramatic decrease in efficiency of training/inference. To address those problems, we introduce a fresh paradigm to integrate label knowledge and further propose a novel model to explicitly and efficiently integrate label knowledge into text representations. Specifically, it encodes texts and label annotations independently and then integrates label knowledge into text representation with an elaborate-designed semantics fusion module. We conduct extensive experiments on three typical span extraction tasks: flat NER, nested NER, and event detection. The empirical results show that 1) our model achieves a new state-of-the-art performance on four benchmarks, and 2) reduces training time and inference time by 76% and 77% on average, respectively, compared with the QA Formalization paradigm.
PRIDE: Predicting Relationships in Conversations
Authors: Paramita Mirza, Andrew Yates, Gerhard Weikum Anna Tigunova
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Automatically extracting interpersonal relationships of conversation interlocutors can enrich personal knowledge bases to enhance personalized search, recommenders and chatbots. To infer speakers' relationships from dialogues we propose PRIDE, a neural multi-label classifier, based on BERT and Transformer for creating a conversation representation. PRIDE utilizes dialogue structure and augments it with external knowledge about speaker features and conversation style. Unlike prior works, we address multi-label prediction of fine-grained relationships. We release large-scale datasets, based on screenplays of movies and TV shows, with directed relationships of conversation participants. Extensive experiments on both datasets show superior performance of PRIDE compared to the state-of-the-art baselines.
Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based Results
Authors: Scott Friedman Ian Magnusson
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Recent transformer-based approaches demonstrate promising results on relational scientific information extraction. Existing datasets focus on high-level description of how research is carried out. Instead we focus on the subtleties of how experimental associations are presented by building SciClaim, a dataset of scientific claims drawn from Social and Behavior Science (SBS), PubMed, and CORD-19 papers. Our novel graph annotation schema incorporates not only coarse-grained entity spans as nodes and relations as edges between them, but also fine-grained attributes that modify entities and their relations, for a total of 12,738 labels in the corpus. By including more label types and more than twice the label density of previous datasets, SciClaim captures causal, comparative, predictive, statistical, and proportional associations over experimental variables along with their qualifications, subtypes, and evidence. We extend work in transformer-based joint entity and relation extraction to effectively infer our schema, showing the promise of fine-grained knowledge graphs in scientific claims and beyond.
Sequential Cross-Document Coreference Resolution
Authors: Shuai Wang, Miguel Ballesteros Emily Allaway
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Relating entities and events in text is a key component of natural language understanding. Cross-document coreference resolution, in particular, is important for the growing interest in multi-document analysis tasks. In this work we propose a new model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference while providing strong evidence of the efficacy of both sequential models and higher-order inference in cross-document settings. Our model incrementally composes mentions into cluster representations and predicts links between a mention and the already constructed clusters, approximating a higher-order model. In addition, we conduct extensive ablation studies that provide new insights into the importance of various inputs and representation types in coreference.
Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT
Authors: Fangyu Liu, Thomas Clark, Ehsan Shareghi, Nigel Collier Zaiqiao Meng
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Infusing factual knowledge into pre-trained models is fundamental for many knowledge-intensive tasks. In this paper, we proposed Mixture-of-Partitions (MoP), an infusion approach that can handle a very large knowledge graph (KG) by partitioning it into smaller sub-graphs and infusing their specific knowledge into various BERT models using lightweight adapters. To leverage the overall factual knowledge for a target task, these sub-graph adapters are further fine-tuned along with the underlying BERT through a mixture layer. We evaluate our MoP with three biomedical BERTs (SciBERT, BioBERT, PubmedBERT) on six downstream tasks (inc. NLI, QA, Classification), and the results show that our MoP consistently enhances the underlying BERTs in task performance, and achieves new SOTA performances on five evaluated datasets.
Filling the Gaps in Ancient Akkadian Texts: A Masked Language Modelling Approach
Authors: Benny Saret, Asaf Yehudai, Wayne Horowitz, Nathan Wasserman, Gabriel Stanovsky Koren Lazar
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: We present models which complete missing text given transliterations of ancient Mesopotamian documents, originally written on cuneiform clay tablets (2500 BCE - 100 CE). Due to the tablets' deterioration, scholars often rely on contextual cues to manually fill in missing parts in the text in a subjective and time-consuming process. We identify that this challenge can be formulated as a masked language modelling task, used mostly as a pretraining objective for contextualized language models. Following, we develop several architectures focusing on the Akkadian language, the lingua franca of the time. We find that despite data scarcity (1M tokens) we can achieve state of the art performance on missing tokens prediction (89% hit@5) using a greedy decoding scheme and pretraining on data from other languages and different time periods. Finally, we conduct human evaluations showing the applicability of our models in assisting experts to transcribe texts in extinct languages.
AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain
Authors: TaeHee Kim, Hyesu Lim, Jaegul Choo Jimin Hong
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: During the fine-tuning phase of transfer learning, the pretrained vocabulary remains unchanged, while model parameters are updated. The vocabulary generated based on the pretrained data is suboptimal for downstream data when domain discrepancy exists. We propose to consider the vocabulary as an optimizable parameter, allowing us to update the vocabulary by expanding it with domain specific vocabulary based on a tokenization statistic. Furthermore, we preserve the embeddings of the added words from overfitting to downstream data by utilizing knowledge learned from a pretrained language model with a regularization term. Our method achieved consistent performance improvements on diverse domains (i.e., biomedical, computer science, news, and reviews).
Can We Improve Model Robustness through Secondary Attribute Counterfactuals?
Authors: Xuezhi Wang, Ben Packer, Nithum Thain, Ed Chi, Alex Beutel Ananth Balashankar
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Developing robust NLP models that perform well on many, even small, slices of data is a significant but important challenge, with implications from fairness to general reliability. To this end, recent research has explored how models rely on spurious correlations, and how counterfactual data augmentation (CDA) can mitigate such issues. In this paper we study how and why modeling counterfactuals over multiple attributes can go significantly further in improving model performance. We propose RDI, a context-aware methodology which takes into account the impact of secondary attributes on the model`s predictions and increases sensitivity for secondary attributes over reweighted counterfactually augmented data. By implementing RDI in the context of toxicity detection, we find that accounting for secondary attributes can significantly improve robustness, with improvements in sliced accuracy on the original dataset up to 7% compared to existing robustness methods. We also demonstrate that RDI generalizes to the coreference resolution task and provide guidelines to extend this to other tasks.
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy
Authors: Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, Alexey Svyatkovskiy Colin Clement
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Statistical language modeling and translation with transformers have found many successful applications in program understanding and generation tasks, setting high benchmarks for tools in modern software development environments. The finite context window of these neural models means, however, that they will be unable to leverage the entire relevant context of large files and packages for any given task. While there are many efforts to extend the context window, we introduce an architecture-independent approach for leveraging the syntactic hierarchies of source code for incorporating entire file-level context into a fixed-length window. Using concrete syntax trees of each source file we extract syntactic hierarchies and integrate them into context window by selectively removing from view more specific, less relevant scopes for a given task. We evaluate this approach on code generation tasks and joint translation of natural language and source code in Python programming language, achieving a new state-of-the-art in code completion and summarization for Python in the CodeXGLUE benchmark. We also introduce new CodeXGLUE benchmarks for user-experience-motivated tasks: code completion with normalized literals, method body completion/code summarization conditioned on file-level context.
Can Language Models be Biomedical Knowledge Bases?
Authors: Jinhyuk Lee, Sean Yi, Minji Jeon, Sungdong Kim, Jaewoo Kang Mujeen Sung
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Pre-trained language models (LMs) have become ubiquitous in solving various natural language processing (NLP) tasks. There has been increasing interest in what knowledge these LMs contain and how we can extract that knowledge, treating LMs as knowledge bases (KBs). While there has been much work on probing LMs in the general domain, there has been little attention to whether these powerful LMs can be used as domain-specific KBs. To this end, we create the BioLAMA benchmark, which is comprised of 49K biomedical factual knowledge triples for probing biomedical LMs. We find that biomedical LMs with recently proposed probing methods can achieve up to 18.51% Acc@5 on retrieving biomedical knowledge. Although this seems promising given the task difficulty, our detailed analyses reveal that most predictions are highly correlated with prompt templates without any subjects, hence producing similar results on each relation and hindering their capabilities to be used as domain-specific KBs. We hope that BioLAMA can serve as a challenging benchmark for biomedical factual probing.
LayoutReader: Pre-training of Text and Layout for Reading Order Detection
Authors: Yiheng Xu, Lei Cui, Jingbo Shang, Furu Wei Zilong Wang
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Reading order detection is the cornerstone to understanding visually-rich documents (e.g., receipts and forms). Unfortunately, no existing work took advantage of advanced deep learning models because it is too laborious to annotate a large enough dataset. We observe that the reading order of WORD documents is embedded in their XML metadata; meanwhile, it is easy to convert WORD documents to PDFs or images. Therefore, in an automated manner, we construct ReadingBank, a benchmark dataset that contains reading order, text, and layout information for 500,000 document images covering a wide spectrum of document types. This first-ever large-scale dataset unleashes the power of deep neural networks for reading order detection. Specifically, our proposed LayoutReader captures the text and layout information for reading order prediction using the seq2seq model. It performs almost perfectly in reading order detection and significantly improves both open-source and commercial OCR engines in ordering text lines in their results in our experiments. The dataset and models are publicly available at r̆lhttps://aka.ms/layoutreader.
Region under Discussion for visual dialog
Authors: Franco M. Luque, Jorge Sánchez, Hernán Maina, Thomas Vadora, Luciana Benotti Mauricio Mazuecos
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Visual Dialog is assumed to require the dialog history to generate correct responses during a dialog. However, it is not clear from previous work how dialog history is needed for visual dialog. In this paper we define what it means for a visual question to require dialog history and we release a subset of the Guesswhat?! questions for which their dialog history completely changes their responses. We propose a novel interpretable representation that visually grounds dialog history: the Region under Discussion. It constrains the image`s spatial features according to a semantic representation of the history inspired by the information structure notion of Question under Discussion.We evaluate the architecture on task-specific multimodal models and the visual transformer model LXMERT.
Learning grounded word meaning representations on similarity graphs
Authors: Herwig Wendt, Pau Batlle Franch Mariella Dimiccoli
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: This paper introduces a novel approach to learn visually grounded meaning representations of words as low-dimensional node embeddings on an underlying graph hierarchy. The lower level of the hierarchy models modality-specific word representations, conditioned to another modality, through dedicated but communicating graphs, while the higher level puts these representations together on a single graph to learn a representation jointly from both modalities. The topology of each graph models similarity relations among words, and is estimated jointly with the graph embedding. The assumption underlying this model is that words sharing similar meaning correspond to communities in an underlying graph in a low-dimensional space. We named this model Hierarchical Multi-Modal Similarity Graph Embedding (HM-SGE). Experimental results validate the ability of HM-SGE to simulate human similarity judgments and concept categorization, outperforming the state of the art.
WhyAct: Identifying Action Reasons in Lifestyle Vlogs
Authors: Santiago Castro, Hanwen Miao, Weiji Li, Rada Mihalcea Oana Ignat
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: We aim to automatically identify human action reasons in online videos. We focus on the widespread genre of lifestyle vlogs, in which people perform actions while verbally describing them. We introduce and make publicly available the WhyAct dataset, consisting of 1,077 visual actions manually annotated with their reasons. We describe a multimodal model that leverages visual and textual information to automatically infer the reasons corresponding to an action presented in the video.
Genre as Weak Supervision for Cross-lingual Dependency Parsing
Authors: Rob van der Goot, Barbara Plank Max Müller-Eberstein
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Recent work has shown that monolingual masked language models learn to represent data-driven notions of language variation which can be used for domain-targeted training data selection. Dataset genre labels are already frequently available, yet remain largely unexplored in cross-lingual setups. We harness this genre metadata as a weak supervision signal for targeted data selection in zero-shot dependency parsing. Specifically, we project treebank-level genre information to the finer-grained sentence level, with the goal to amplify information implicitly stored in unsupervised contextualized representations. We demonstrate that genre is recoverable from multilingual contextual embeddings and that it provides an effective signal for training data selection in cross-lingual, zero-shot scenarios. For 12 low-resource language treebanks, six of which are test-only, our genre-specific methods significantly outperform competitive baselines as well as recent embedding-based methods for data selection. Moreover, genre-based data selection provides new state-of-the-art results for three of these target languages.
On the Relation between Syntactic Divergence and Zero-Shot Performance
Authors: Dmitry Nikolaev, Taelin Karidi, Omri Abend Ofir Arviv
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: We explore the link between the extent to which syntactic relations are preserved in translation and the ease of correctly constructing a parse tree in a zero-shot setting. While previous work suggests such a relation, it tends to focus on the macro level and not on the level of individual edges---a gap we aim to address. As a test case, we take the transfer of Universal Dependencies (UD) parsing from English to a diverse set of languages and conduct two sets of experiments. In one, we analyze zero-shot performance based on the extent to which English source edges are preserved in translation. In another, we apply three linguistically motivated transformations to UD, creating more cross-lingually stable versions of it, and assess their zero-shot parsability. In order to compare parsing performance across different schemes, we perform extrinsic evaluation on the downstream task of cross-lingual relation extraction (RE) using a subset of a standard English RE benchmark translated to Russian and Korean. In both sets of experiments, our results suggest a strong relation between cross-lingual stability and zero-shot parsing performance.
Improved Latent Tree Induction with Distant Supervision via Span Constraints
Authors: Andrew Drozdov, Jay Yoon Lee, Tim O′Gorman, Subendhu Rongali, Dylan Finkbeiner, Shilpa Suresh, Mohit Iyyer, Andrew McCallum Zhiyang Xu
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: For over thirty years, researchers have developed and analyzed methods for latent tree induction as an approach for unsupervised syntactic parsing. Nonetheless, modern systems still do not perform well enough compared to their supervised counterparts to have any practical use as structural annotation of text. In this work, we present a technique that uses distant supervision in the form of span constraints (i.e. phrase bracketing) to improve performance in unsupervised constituency parsing. Using a relatively small number of span constraints we can substantially improve the output from DIORA, an already competitive unsupervised parsing system. Compared with full parse tree annotation, span constraints can be acquired with minimal effort, such as with a lexicon derived from Wikipedia, to find exact text matches. Our experiments show span constraints based on entities improves constituency parsing on English WSJ Penn Treebank by more than 5 F1. Furthermore, our method extends to any domain where span constraints are easily attainable, and as a case study we demonstrate its effectiveness by parsing biomedical text from the CRAFT dataset.
Aligning Multidimensional Worldviews and Discovering Ideological Differences
Authors: Adarsh Mathew, James Evans Jeremiah Milbauer
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: The Internet is home to thousands of communities, each with their own unique worldview and associated ideological differences. With new communities constantly emerging and serving as ideological birthplaces, battlegrounds, and bunkers, it is critical to develop a framework for understanding worldviews and ideological distinction. Most existing work, however, takes a predetermined view based on political polarization: the “right vs. left” dichotomy of U.S. politics. In reality, both political polarization -- and worldviews more broadly -- transcend one-dimensional difference, and deserve a more complete analysis. Extending the ability of word embedding models to capture the semantic and cultural characteristics of their training corpora, we propose a novel method for discovering the multifaceted ideological and worldview characteristics of communities. Using over 1B comments collected from the largest communities on Reddit.com representing ~40% of Reddit activity, we demonstrate the efficacy of this approach to uncover complex ideological differences across multiple axes of polarization.
Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts
Authors: Maarten Sap, Alan Ritter, Mark Riedl Ashutosh Baheti
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Dialogue models trained on human conversations inadvertently learn to generate toxic responses. In addition to producing explicitly offensive utterances, these models can also implicitly insult a group or individual by aligning themselves with an offensive statement. To better understand the dynamics of contextually offensive language, we investigate the stance of dialogue model responses in offensive Reddit conversations. Specifically, we create ToxiChat, a crowd-annotated dataset of 2,000 Reddit threads and model responses labeled with offensive language and stance. Our analysis reveals that 42% of human responses agree with toxic comments, whereas only 13% agree with safe comments. This undesirable behavior is learned by neural dialogue models, such as DialoGPT, which we show are two times more likely to agree with offensive comments. To enable automatic detection of offensive language, we fine-tuned transformer-based classifiers on ToxiChat that achieve 0.71 F1 for offensive labels and 0.53 Macro-F1 for stance labels. Finally, we quantify the effectiveness of controllable text generation (CTG) methods to mitigate the tendency of neural dialogue models to agree with offensive comments. Compared to the baseline, our best CTG model achieves a 19% reduction in agreement with offensive comments and produces 29% fewer offensive replies. Our work highlights the need for further efforts to characterize and analyze inappropriate behavior in dialogue models, in order to help make them safer.
Multi-Modal Open-Domain Dialogue
Authors: Eric Michael Smith, Da Ju, Jason Weston Kurt Shuster
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Recent work in open-domain conversational agents has demonstrated that significant improvements in humanness and user preference can be achieved via massive scaling in both pre-training data and model size (Adiwardana et al., 2020; Roller et al., 2020). However, if we want to build agents with human-like abilities, we must expand beyond handling just text. A particularly important topic is the ability to see images and communicate about what is perceived. With the goal of getting humans to engage in multi-modal dialogue, we investigate combining components from state-of-the-art open-domain dialogue agents with those from state-of-the-art vision models. We study incorporating different image fusion schemes and domain-adaptive pre-training and fine-tuning strategies, and show that our best resulting model outperforms strong existing models in multi-modal dialogue while simultaneously performing as well as its predecessor (text-only) BlenderBot (Roller et al., 2020) in text-based conversation. We additionally investigate and incorporate safety components in our final model, and show that such efforts do not diminish model performance with respect to human preference.
A Label-Aware BERT Attention Network for Zero-Shot Multi-Intent Detection in Spoken Language Understanding
Authors: Ruolin Su, Biing Juang Ting-Wei Wu
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: With the early success of query-answer assistants such as Alexa and Siri, research attempts to expand system capabilities of handling service automation are now abundant. However, preliminary systems have quickly found the inadequacy in relying on simple classification techniques to effectively accomplish the automation task. The main challenge is that the dialogue often involves complexity in user`s intents (or purposes) which are multiproned, subject to spontaneous change, and difficult to track. Furthermore, public datasets have not considered these complications and the general semantic annotations are lacking which may result in zero-shot problem. Motivated by the above, we propose a Label-Aware BERT Attention Network (LABAN) for zero-shot multi-intent detection. We first encode input utterances with BERT and construct a label embedded space by considering embedded semantics in intent labels. An input utterance is then classified based on its projection weights on each intent embedding in this embedded space. We show that it successfully extends to few/zero-shot setting where part of intent labels are unseen in training data, by also taking account of semantics in these unseen intent labels. Experimental results show that our approach is capable of detecting many unseen intent labels correctly. It also achieves the state-of-the-art performance on five multi-intent datasets in normal cases.
Zero-Shot Dialogue Disentanglement by Self-Supervised Entangled Response Selection
Authors: Alexander Rudnicky Ta-Chung Chi
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Dialogue disentanglement aims to group utterances in a long and multi-participant dialogue into threads. This is useful for discourse analysis and downstream applications such as dialogue response selection, where it can be the first step to construct a clean context/response set. Unfortunately, labeling all \textitreply-to links takes quadratic effort w.r.t the number of utterances: an annotator must check all preceding utterances to identify the one to which the current utterance is a reply. In this paper, we are the first to propose a \textbfzero-shot dialogue disentanglement solution. Firstly, we train a model on a multi-participant response selection dataset harvested from the web which is not annotated; we then apply the trained model to perform zero-shot dialogue disentanglement. Without any labeled data, our model can achieve a cluster F1 score of 25. We also fine-tune the model using various amounts of labeled data. Experiments show that with only 10% of the data, we achieve nearly the same performance of using the full dataset.
SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations
Authors: Seungwhan Moon, Alborz Geramifard, Babak Damavandi Satwik Kottur
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Next generation task-oriented dialog systems need to understand conversational contexts with their perceived surroundings, to effectively help users in the real-world multimodal environment. Existing task-oriented dialog datasets aimed towards virtual assistance fall short and do not situate the dialog in the user`s multimodal context. To overcome, we present a new dataset for Situated and Interactive Multimodal Conversations, SIMMC 2.0, which includes 11K task-oriented user\ensuremath<-\ensuremath>assistant dialogs (117K utterances) in the shopping domain, grounded in immersive and photo-realistic scenes. The dialogs are collection using a two-phase pipeline: (1) A novel multimodal dialog simulator generates simulated dialog flows, with an emphasis on diversity and richness of interactions, (2) Manual paraphrasing of generating utterances to draw from natural language distribution. We provide an in-depth analysis of the collected dataset, and describe in detail the four main benchmark tasks we propose for SIMMC 2.0. Our baseline model, powered by the state-of-the-art language model, shows promising results, and highlights new challenges and directions for the community to study.
RAST: Domain-Robust Dialogue Rewriting as Sequence Tagging
Authors: Linfeng Song, Liwei Wang, Kun Xu, Zhaopeng Tu, Dong Yu Jie Hao
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: The task of dialogue rewriting aims to reconstruct the latest dialogue utterance by copying the missing content from the dialogue context. Until now, the existing models for this task suffer from the robustness issue, i.e., performances drop dramatically when testing on a different dataset. We address this robustness issue by proposing a novel sequence-tagging-based model so that the search space is significantly reduced, yet the core of this task is still well covered. As a common issue of most tagging models for text generation, the model`s outputs may lack fluency. To alleviate this issue, we inject the loss signal from BLEU or GPT-2 under a REINFORCE framework. Experiments show huge improvements of our model over the current state-of-the-art systems when transferring to another dataset.
MRF-Chat: Improving Dialogue with Markov Random Fields
Authors: Matthew Huggins, Cynthia Breazeal, Hae Won Park Ishaan Grover
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Recent state-of-the-art approaches in open-domain dialogue include training end-to-end deep-learning models to learn various conversational features like emotional content of response, symbolic transitions of dialogue contexts in a knowledge graph and persona of the agent and the user, among others. While neural models have shown reasonable results, modelling the cognitive processes that humans use when conversing with each other may improve the agent`s quality of responses. A key element of natural conversation is to tailor one`s response such that it accounts for concepts that the speaker and listener may or may not know and the contextual relevance of all prior concepts used in conversation. We show that a rich representation and explicit modeling of these psychological processes can improve predictions made by existing neural network models. In this work, we propose a novel probabilistic approach using Markov Random Fields (MRF) to augment existing deep-learning methods for improved next utterance prediction. Using human and automatic evaluations, we show that our augmentation approach significantly improves the performance of existing state-of-the-art retrieval models for open-domain conversational agents.
Dialogue State Tracking with a Language Model using Schema-Driven Prompting
Authors: Hao Cheng, Mari Ostendorf Chia-Hsuan Lee
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Task-oriented conversational systems often use dialogue state tracking to represent the user`s intentions, which involves filling in values of pre-defined slots. Many approaches have been proposed, often using task-specific architectures with special-purpose classifiers. Recently, good results have been obtained using more general architectures based on pretrained language models. Here, we introduce a new variation of the language modeling approach that uses schema-driven prompting to provide task-aware history encoding that is used for both categorical and non-categorical slots. We further improve performance by augmenting the prompting with schema descriptions, a naturally occurring source of in-domain knowledge. Our purely generative system achieves state-of-the-art performance on MultiWOZ 2.2 and achieves competitive performance on two other benchmarks: MultiWOZ 2.1 and M2M. The data and code will be available at r̆lhttps://github.com/chiahsuan156/DST-as-Prompting.
Signed Coreference Resolution
Authors: Kenneth DeHaan, Malihe Alikhani Kayo Yin
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Coreference resolution is key to many natural language processing tasks and yet has been relatively unexplored in Sign Language Processing. In signed languages, space is primarily used to establish reference. Solving coreference resolution for signed languages would not only enable higher-level Sign Language Processing systems, but also enhance our understanding of language in different modalities and of situated references, which are key problems in studying grounded language. In this paper, we: (1) introduce Signed Coreference Resolution (SCR), a new challenge for coreference modeling and Sign Language Processing; (2) collect an annotated corpus of German Sign Language with gold labels for coreference together with an annotation software for the task; (3) explore features of hand gesture, iconicity, and spatial situated properties and move forward to propose a set of linguistically informed heuristics and unsupervised models for the task; (4) put forward several proposals about ways to address the complexities of this challenge effectively.
Consistent Accelerated Inference via Confident Adaptive Transformers
Authors: Adam Fisch, Tommi Jaakkola, Regina Barzilay Tal Schuster
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: We develop a novel approach for confidently accelerating inference in the large and expensive multilayer Transformers that are now ubiquitous in natural language processing (NLP). Amortized or approximate computational methods increase efficiency, but can come with unpredictable performance costs. In this work, we present CATs -- Confident Adaptive Transformers -- in which we simultaneously increase computational efficiency, while guaranteeing a specifiable degree of consistency with the original model with high confidence. Our method trains additional prediction heads on top of intermediate layers, and dynamically decides when to stop allocating computational effort to each input using a meta consistency classifier. To calibrate our early prediction stopping rule, we formulate a unique extension of conformal prediction. We demonstrate the effectiveness of this approach on four classification and regression tasks.
Improving and Simplifying Pattern Exploiting Training
Authors: Rakesh R. Menon, Mohit Bansal, Shashank Srivastava, Colin Raffel Derek Tam
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Recently, pre-trained language models (LMs) have achieved strong performance when fine-tuned on difficult benchmarks like SuperGLUE. However, performance can suffer when there are very few labeled examples available for fine-tuning. Pattern Exploiting Training (PET) is a recent approach that leverages patterns for few-shot learning. However, PET uses task-specific unlabeled data. In this paper, we focus on few-shot learning without any unlabeled data and introduce ADAPET, which modifies PET`s objective to provide denser supervision during fine-tuning. As a result, ADAPET outperforms PET on SuperGLUE without any task-specific unlabeled data.