pwc-archive/papers-with-abstracts huggingface dataset:
Semantic Frame Forecast
Authors: Chieh-Yang Huang, Ting-Hao 'Kenneth' Huang
Date: 2021-04-12 00:00:00
Proceeding: NAACL 2021 4
Tasks: ARC

This paper introduces semantic frame forecast, a task that predicts the semantic frames that will occur in the next 10, 100, or even 1,000 sentences in a running story. Prior work focused on predicting the immediate future of a story, such as one to a few sentences ahead. However, when novelists write long stories, generating a few sentences is not enough to help them gain high-level insight to develop the follow-up story. In this paper, we formulate a long story as a sequence of "story blocks," where each block contains a fixed number of sentences (e.g., 10, 100, or 200). This formulation allows us to predict the follow-up story arc beyond the scope of a few sentences. We represent a story block using the term frequencies (TF) of semantic frames in it, normalized by each frame's inverse document frequency (IDF). We conduct semantic frame forecast experiments on 4,794 books from the Bookcorpus and 7,962 scientific abstracts from CODA-19, with block sizes ranging from 5 to 1,000 sentences. The results show that automated models can forecast the follow-up story blocks better than the random, prior, and replay baselines, indicating the task's feasibility. We also learn that the models using the frame representation as features outperform all the existing approaches when the block size is over 150 sentences. The human evaluation also shows that the proposed frame representation, when visualized as word clouds, is comprehensible, representative, and specific to humans. Our code is available at https://github.com/appleternity/FrameForecasting.
Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages
Authors: Gowtham Ramesh, Sumanth Doddapaneni, Aravinth Bheemaraj, Mayank Jobanputra, Raghavan AK, Ajitesh Sharma, Sujit Sahoo, Harshita Diddee, Mahalakshmi J, Divyanshu Kakwani, Navneet Kumar, Aswin Pradeep, Srihari Nagaraj, Kumar Deepak, Vivek Raghavan, Anoop Kunchukuttan, Pratyush Kumar, Mitesh Shantadevi Khapra
Date: 2021-04-12 00:00:00
Tasks: Machine Translation, Multilingual NLP, NMT, Optical Character Recognition (OCR), Sentence

We present Samanantar, the largest publicly available parallel corpora collection for Indic languages. The collection contains a total of 49.7 million sentence pairs between English and 11 Indic languages (from two language families). Specifically, we compile 12.4 million sentence pairs from existing, publicly-available parallel corpora, and additionally mine 37.4 million sentence pairs from the web, resulting in a 4x increase. We mine the parallel sentences from the web by combining many corpora, tools, and methods: (a) web-crawled monolingual corpora, (b) document OCR for extracting sentences from scanned documents, (c) multilingual representation models for aligning sentences, and (d) approximate nearest neighbor search for searching in a large collection of sentences. Human evaluation of samples from the newly mined corpora validate the high quality of the parallel sentences across 11 languages. Further, we extract 83.4 million sentence pairs between all 55 Indic language pairs from the English-centric parallel corpus using English as the pivot language. We trained multilingual NMT models spanning all these languages on Samanantar, which outperform existing models and baselines on publicly available benchmarks, such as FLORES, establishing the utility of Samanantar. Our data and models are available publicly at https://ai4bharat.iitm.ac.in/samanantar and we hope they will help advance research in NMT and multilingual NLP for Indic languages.
DATE: Detecting Anomalies in Text via Self-Supervision of Transformers
Authors: Andrei Manolache, Florin Brad, Elena Burceanu
Date: 2021-04-12 00:00:00
Proceeding: NAACL 2021 4
Tasks: Anomaly Detection

Leveraging deep learning models for Anomaly Detection (AD) has seen widespread use in recent years due to superior performances over traditional methods. Recent deep methods for anomalies in images learn better features of normality in an end-to-end self-supervised setting. These methods train a model to discriminate between different transformations applied to visual data and then use the output to compute an anomaly score. We use this approach for AD in text, by introducing a novel pretext task on text sequences. We learn our DATE model end-to-end, enforcing two independent and complementary self-supervision signals, one at the token-level and one at the sequence-level. Under this new task formulation, we show strong quantitative and qualitative results on the 20Newsgroups and AG News datasets. In the semi-supervised setting, we outperform state-of-the-art results by +13.5% and +6.9%, respectively (AUROC). In the unsupervised configuration, DATE surpasses all other methods even when 10% of its training data is contaminated with outliers (compared with 0% for the others).
WHOSe Heritage: Classification of UNESCO World Heritage "Outstanding Universal Value" Documents with Soft Labels
Authors: Nan Bai, Renqian Luo, Pirouz Nourian, Ana Pereira Roders
Date: 2021-04-12 00:00:00
Tasks: General Classification, Management, Multi-Label Classification

The UNESCO World Heritage List (WHL) includes the exceptionally valuable cultural and natural heritage to be preserved for mankind. Evaluating and justifying the Outstanding Universal Value (OUV) is essential for each site inscribed in the WHL, and yet a complex task, even for experts, since the selection criteria of OUV are not mutually exclusive. Furthermore, manual annotation of heritage values and attributes from multi-source textual data, which is currently dominant in heritage studies, is knowledge-demanding and time-consuming, impeding systematic analysis of such authoritative documents in terms of their implications on heritage management. This study applies state-of-the-art NLP models to build a classifier on a new dataset containing Statements of OUV, seeking an explainable and scalable automation tool to facilitate the nomination, evaluation, research, and monitoring processes of World Heritage sites. Label smoothing is innovatively adapted to improve the model performance by adding prior inter-class relationship knowledge to generate soft labels. The study shows that the best models fine-tuned from BERT and ULMFiT can reach 94.3% top-3 accuracy. A human study with expert evaluation on the model prediction shows that the models are sufficiently generalizable. The study is promising to be further developed and applied in heritage research and practice.
Investigating Methods to Improve Language Model Integration for Attention-based Encoder-Decoder ASR Models
Authors: Mohammad Zeineldeen, Aleksandr Glushko, Wilfried Michel, Albert Zeyer, Ralf Schlüter, Hermann Ney
Date: 2021-04-12 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling

Attention-based encoder-decoder (AED) models learn an implicit internal language model (ILM) from the training transcriptions. The integration with an external LM trained on much more unpaired text usually leads to better performance. A Bayesian interpretation as in the hybrid autoregressive transducer (HAT) suggests dividing by the prior of the discriminative acoustic model, which corresponds to this implicit LM, similarly as in the hybrid hidden Markov model approach. The implicit LM cannot be calculated efficiently in general and it is yet unclear what are the best methods to estimate it. In this work, we compare different approaches from the literature and propose several novel methods to estimate the ILM directly from the AED model. Our proposed methods outperform all previous approaches. We also investigate other methods to suppress the ILM mainly by decreasing the capacity of the AED model, limiting the label context, and also by training the AED model together with a pre-existing LM.
Self-Training with Weak Supervision
Authors: Giannis Karamanolakis, Subhabrata Mukherjee, Guoqing Zheng, Ahmed Hassan Awadallah
Date: 2021-04-12 00:00:00
Proceeding: NAACL 2021 4
Tasks: text-classification, Text Classification

State-of-the-art deep neural networks require large-scale labeled training data that is often expensive to obtain or not available for many tasks. Weak supervision in the form of domain-specific rules has been shown to be useful in such settings to automatically generate weakly labeled training data. However, learning with weak rules is challenging due to their inherent heuristic and noisy nature. An additional challenge is rule coverage and overlap, where prior work on weak supervision only considers instances that are covered by weak rules, thus leaving valuable unlabeled data behind. In this work, we develop a weak supervision framework (ASTRA) that leverages all the available data for a given task. To this end, we leverage task-specific unlabeled data through self-training with a model (student) that considers contextualized representations and predicts pseudo-labels for instances that may not be covered by weak rules. We further develop a rule attention network (teacher) that learns how to aggregate student pseudo-labels with weak rule labels, conditioned on their fidelity and the underlying context of an instance. Finally, we construct a semi-supervised learning objective for end-to-end training with unlabeled data, domain-specific rules, and a small amount of labeled data. Extensive experiments on six benchmark datasets for text classification demonstrate the effectiveness of our approach with significant improvements over state-of-the-art baselines.
Fine-Tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets
Authors: Max Fleming, Priyanka Dondeti, Caitlin N. Dreisbach, Adam Poliak
Date: 2021-04-12 00:00:00
Proceeding: NAACL (SMM4H) 2021 6

We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social Media Mining for Health Applications (SMM4H) shared tasks. Our system is based on fine-tuning Distill- BERT on each task, as well as first fine-tuning the model on the other task. We explore how much fine-tuning is necessary for accurately classifying tweets as containing self-reported COVID-19 symptoms (Task 5) or whether a tweet related to COVID-19 is self-reporting, non-personal reporting, or a literature/news mention of the virus (Task 6).
CNN Encoding of Acoustic Parameters for Prominence Detection
Authors: Kamini Sabu, Mithilesh Vaidya, Preeti Rao
Date: 2021-04-12 00:00:00
Tasks: Attribute

Expressive reading, considered the defining attribute of oral reading fluency, comprises the prosodic realization of phrasing and prominence. In the context of evaluating oral reading, it helps to establish the speaker's comprehension of the text. We consider a labeled dataset of children's reading recordings for the speaker-independent detection of prominent words using acoustic-prosodic and lexico-syntactic features. A previous well-tuned random forest ensemble predictor is replaced by an RNN sequence classifier to exploit potential context dependency across the longer utterance. Further, deep learning is applied to obtain word-level features from low-level acoustic contours of fundamental frequency, intensity and spectral shape in an end-to-end fashion. Performance comparisons are presented across the different feature types and across different feature learning architectures for prominent word prediction to draw insights wherever possible.
Developing Annotated Resources for Internal Displacement Monitoring
Authors: Fabio Poletto, Yunbai Zhang, Andre Panisson, Yelena Mejova, Daniela Paolotti, Sylvain Ponserre
Date: 2021-04-12 00:00:00
Tasks: Document Classification

This paper describes in details the design and development of a novel annotation framework and of annotated resources for Internal Displacement, as the outcome of a collaboration with the Internal Displacement Monitoring Centre, aimed at improving the accuracy of their monitoring platform IDETECT. The schema includes multi-faceted description of the events, including cause, quantity of people displaced, location and date. Higher-order facets aimed at improving the information extraction, such as document relevance and type, are proposed. We also report a case study of machine learning application to the document classification tasks. Finally, we discuss the importance of standardized schema in dataset benchmark development and its impact on the development of reliable disaster monitoring infrastructure.
Multilingual Language Models Predict Human Reading Behavior
Authors: Nora Hollenstein, Federico Pirovano, Ce Zhang, Lena Jäger, Lisa Beinborn
Date: 2021-04-12 00:00:00
Proceeding: NAACL 2021 4
Tasks: Sentence

We analyze if large language models are able to predict patterns of human reading behavior. We compare the performance of language-specific and multilingual pretrained transformer models to predict reading time measures reflecting natural human sentence processing on Dutch, English, German, and Russian texts. This results in accurate models of human reading behavior, which indicates that transformer models implicitly encode relative importance in language in a way that is comparable to human processing mechanisms. We find that BERT and XLM models successfully predict a range of eye tracking features. In a series of experiments, we analyze the cross-domain and cross-language abilities of these models and show how they reflect human sentence processing.
Breaking Community Boundary: Comparing Academic and Social Communication Preferences regarding Global Pandemics
Authors: Qingqing Zhou, Chengzhi Zhang
Date: 2021-04-12 00:00:00
Tasks: Articles

The global spread of COVID-19 has caused pandemics to be widely discussed. This is evident in the large number of scientific articles and the amount of user-generated content on social media. This paper aims to compare academic communication and social communication about the pandemic from the perspective of communication preference differences. It aims to provide information for the ongoing research on global pandemics, thereby eliminating knowledge barriers and information inequalities between the academic and the social communities. First, we collected the full text and the metadata of pandemic-related articles and Twitter data mentioning the articles. Second, we extracted and analyzed the topics and sentiment tendencies of the articles and related tweets. Finally, we conducted pandemic-related differential analysis on the academic community and the social community. We mined the resulting data to generate pandemic communication preferences (e.g., information needs, attitude tendencies) of researchers and the public, respectively. The research results from 50,338 articles and 927,266 corresponding tweets mentioning the articles revealed communication differences about global pandemics between the academic and the social communities regarding the consistency of research recognition and the preferences for particular research topics. The analysis of large-scale pandemic-related tweets also confirmed the communication preference differences between the two communities.
The Great Misalignment Problem in Human Evaluation of NLP Methods
Authors: Mika Hämäläinen, Khalid Alnajjar
Date: 2021-04-12 00:00:00
Proceeding: EACL (HumEval) 2021 4

We outline the Great Misalignment Problem in natural language processing research, this means simply that the problem definition is not in line with the method proposed and the human evaluation is not in line with the definition nor the method. We study this misalignment problem by surveying 10 randomly sampled papers published in ACL 2020 that report results with human evaluation. Our results show that only one paper was fully in line in terms of problem definition, method and evaluation. Only two papers presented a human evaluation that was in line with what was modeled in the method. These results highlight that the Great Misalignment Problem is a major one and it affects the validity and reproducibility of results obtained by a human evaluation.
Stay Together: A System for Single and Split-antecedent Anaphora Resolution
Authors: Juntao Yu, Nafise Sadat Moosavi, Silviu Paun, Massimo Poesio
Date: 2021-04-12 00:00:00
Proceeding: NAACL 2021 4

The state-of-the-art on basic, single-antecedent anaphora has greatly improved in recent years. Researchers have therefore started to pay more attention to more complex cases of anaphora such as split-antecedent anaphora, as in Time-Warner is considering a legal challenge to Telecommunications Inc's plan to buy half of Showtime Networks Inc-a move that could lead to all-out war between the two powerful companies. Split-antecedent anaphora is rarer and more complex to resolve than single-antecedent anaphora; as a result, it is not annotated in many datasets designed to test coreference, and previous work on resolving this type of anaphora was carried out in unrealistic conditions that assume gold mentions and/or gold split-antecedent anaphors are available. These systems also focus on split-antecedent anaphors only. In this work, we introduce a system that resolves both single and split-antecedent anaphors, and evaluate it in a more realistic setting that uses predicted mentions. We also start addressing the question of how to evaluate single and split-antecedent anaphors together using standard coreference evaluation metrics.
A Hierarchical State-Machine-Based Framework for Platoon Manoeuvre Descriptions
Authors: Corvin Deboeser, Jordan Ivanchev, Thomas Braud, Alois Knoll, David Eckhoff, Alberto Sangiovanni-Vincentelli
Date: 2021-04-12 00:00:00

This paper introduces the SEAD framework that simplifies the process of designing and describing autonomous vehicle platooning manoeuvres. Although a large body of research has been formulating platooning manoeuvres, it is still challenging to design, describe, read, and understand them. This difficulty largely arises from missing formalisation. To fill this gap, we analysed existing ways of describing manoeuvres, derived the causes of difficulty, and designed a framework that simplifies the manoeuvre design process. Alongside, a Manoeuvre Design Language was developed to structurally describe manoeuvres in a machine-readable format. Unlike state-of-the-art manoeuvre descriptions that require one state machine for every participating vehicle, the SEAD framework allows describing any manoeuvre from the single perspective of the platoon leader. %As a proof of concept, the proposed framework was implemented in the mixed traffic simulation environment BEHAVE for an autonomous highway scenario. Using this framework, we implemented several manoeuvres as they were described in literature. To demonstrate the applicability of the framework, an experiment was performed to evaluate the execution time performance of multiple alternatives of the Join-Middle manoeuvre. This proof-of-concept experiment revealed that the manoeuvre execution time can be reduced by 28 \% through parallelising various steps without considerable secondary effects. We hope that the SEAD framework will pave the way for further research in the area of new manoeuvre design and optimisation by largely simplifying and unifying platooning manoeuvre representation.
Building a Swedish Open-Domain Conversational Language Model
Authors: Tobias Norlund, Agnes Stenbom
Date: 2021-04-12 00:00:00
Proceeding: NoDaLiDa 2021 5
Tasks: Language Modeling, Language Modelling, model

We present on-going work of evaluating the, to our knowledge, first large generative language model trained to converse in Swedish, using data from the online discussion forum Flashback. We conduct a human evaluation pilot study that indicates the model is often able to respond to conversations in both a human-like and informative manner, on a diverse set of topics. While data from online forums can be useful to build conversational systems, we reflect on the negative consequences that incautious application might have, and the need for taking active measures to safeguard against them.
Learning to Remove: Towards Isotropic Pre-trained BERT Embedding
Authors: Yuxin Liang, Rui Cao, Jie Zheng, Jie Ren, Ling Gao
Date: 2021-04-12 00:00:00
Tasks: Semantic Textual Similarity, Word Similarity

Pre-trained language models such as BERT have become a more common choice of natural language processing (NLP) tasks. Research in word representation shows that isotropic embeddings can significantly improve performance on downstream tasks. However, we measure and analyze the geometry of pre-trained BERT embedding and find that it is far from isotropic. We find that the word vectors are not centered around the origin, and the average cosine similarity between two random words is much higher than zero, which indicates that the word vectors are distributed in a narrow cone and deteriorate the representation capacity of word embedding. We propose a simple, and yet effective method to fix this problem: remove several dominant directions of BERT embedding with a set of learnable weights. We train the weights on word similarity tasks and show that processed embedding is more isotropic. Our method is evaluated on three standardized tasks: word similarity, word analogy, and semantic textual similarity. In all tasks, the word embedding processed by our method consistently outperforms the original embedding (with average improvement of 13% on word analogy and 16% on semantic textual similarity) and two baseline methods. Our method is also proven to be more robust to changes of hyperparameter.
Factual Probing Is [MASK]: Learning vs. Learning to Recall
Authors: Zexuan Zhong, Dan Friedman, Danqi Chen
Date: 2021-04-12 00:00:00
Proceeding: NAACL 2021 4
Tasks: Language Modelling

Petroni et al. (2019) demonstrated that it is possible to retrieve world facts from a pre-trained language model by expressing them as cloze-style prompts and interpret the model's prediction accuracy as a lower bound on the amount of factual information it encodes. Subsequent work has attempted to tighten the estimate by searching for better prompts, using a disjoint set of facts as training data. In this work, we make two complementary contributions to better understand these factual probing techniques. First, we propose OptiPrompt, a novel and efficient method which directly optimizes in continuous embedding space. We find this simple method is able to predict an additional 6.4% of facts in the LAMA benchmark. Second, we raise a more important question: Can we really interpret these probing results as a lower bound? Is it possible that these prompt-search methods learn from the training data too? We find, somewhat surprisingly, that the training data used by these methods contains certain regularities of the underlying fact distribution, and all the existing prompt methods, including ours, are able to exploit them for better fact prediction. We conduct a set of control experiments to disentangle "learning" from "learning to recall", providing a more detailed picture of what different prompts can reveal about pre-trained language models.
SuperSim: a test set for word similarity and relatedness in Swedish
Authors: Simon Hengchen, Nina Tahmasebi
Date: 2021-04-12 00:00:00
Proceeding: NoDaLiDa 2021 5
Tasks: Word Similarity

Language models are notoriously difficult to evaluate. We release SuperSim, a large-scale similarity and relatedness test set for Swedish built with expert human judgments. The test set is composed of 1,360 word-pairs independently judged for both relatedness and similarity by five annotators. We evaluate three different models (Word2Vec, fastText, and GloVe) trained on two separate Swedish datasets, namely the Swedish Gigaword corpus and a Swedish Wikipedia dump, to provide a baseline for future comparison. We release the fully annotated test set, code, baseline models, and data.