pwc-archive/papers-with-abstracts huggingface dataset:
SSN\_NLP\_MLRG at SemEval-2020 Task 12: Offensive Language Identification in English, Danish, Greek Using BERT and Machine Learning Approach
Authors: A Kalaivani, Thenmozhi D.
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Language Identification

Offensive language identification is to detect the hurtful tweets, derogatory comments, swear words on social media. As an emerging growth of social media communication, offensive language detection has received more attention in the last years; we focus to perform the task on English, Danish and Greek. We have investigated which can be effect more on pre-trained models BERT (Bidirectional Encoder Representation from Transformer) and Machine Learning Approaches. Our investigation shows the difference performance between the three languages and to identify the best performance is evaluated by the classification algorithms. In the shared task SemEval-2020, our team SSN{\_}NLP{\_}MLRG submitted for three languages that are Subtasks A, B, C in English, Subtask A in Danish and Subtask A in Greek. Our team SSN{\_}NLP{\_}MLRG obtained the F1 Scores as 0.90, 0.61, 0.52 for the Subtasks A, B, C in English, 0.56 for the Subtask A in Danish and 0.67 for the Subtask A in Greek respectively.
SU-NLP at SemEval-2020 Task 12: Offensive Language IdentifiCation in Turkish Tweets
Authors: Anil Ozdemir, Reyyan Yeniterzi
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Language Identification, Word Embeddings

This paper summarizes our group{'}s efforts in the offensive language identification shared task, which is organized as part of the International Workshop on Semantic Evaluation (Sem-Eval2020). Our final submission system is an ensemble of three different models, (1) CNN-LSTM, (2) BiLSTM-Attention and (3) BERT. Word embeddings, which were pre-trained on tweets, are used while training the first two models. BERTurk, which is the first BERT model for Turkish, is also explored. Our final submitted approach ranked as the second best model in the Turkish sub-task.
TAC at SemEval-2020 Task 12: Ensembling Approach for Multilingual Offensive Language Identification in Social Media
Authors: Talha Anwar, Omer Baig
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: BIG-bench Machine Learning, Deep Learning, Language Identification

Usage of offensive language on social media is getting more common these days, and there is a need of a mechanism to detect it and control it. This paper deals with offensive language detection in five different languages; English, Arabic, Danish, Greek and Turkish. We presented an almost similar ensemble pipeline comprised of machine learning and deep learning models for all five languages. Three machine learning and four deep learning models were used in the ensemble. In the OffensEval-2020 competition our model achieved F1-score of 0.85, 0.74, 0.68, 0.81, and 0.9 for Arabic, Turkish, Danish, Greek and English language tasks respectively.
Team Rouges at SemEval-2020 Task 12: Cross-lingual Inductive Transfer to Detect Offensive Language
Authors: Tanvi Dadu, Kartikey Pant
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Language Identification, Position, XLM-R, Zero-Shot Learning

With the growing use of social media and its availability, many instances of the use of offensive language have been observed across multiple languages and domains. This phenomenon has given rise to the growing need to detect the offensive language used in social media cross-lingually. In OffensEval 2020, the organizers have released the multilingual Offensive Language Identification Dataset (mOLID), which contains tweets in five different languages, to detect offensive language. In this work, we introduce a cross-lingual inductive approach to identify the offensive language in tweets using the contextual word embedding XLM-RoBERTa (XLM-R). We show that our model performs competitively on all five languages, obtaining the fourth position in the English task with an F1-score of 0.919 and eighth position in the Turkish task with an F1-score of 0.781. Further experimentation proves that our model works competitively in a zero-shot learning environment, and is extensible to other languages.
TECHSSN at SemEval-2020 Task 12: Offensive Language Detection Using BERT Embeddings
Authors: Rajalakshmi Sivanaiah, Angel Suseelan, S Milton Rajendram, Mirnalinee T.t.
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Language Identification

This paper describes the work of identifying the presence of offensive language in social media posts and categorizing a post as targeted to a particular person or not. The work developed by team TECHSSN for solving the Multilingual Offensive Language Identification in Social Media (Task 12) in SemEval-2020 involves the use of deep learning models with BERT embeddings. The dataset is preprocessed and given to a Bidirectional Encoder Representations from Transformers (BERT) model with pretrained weight vectors. The model is retrained and the weights are learned for the offensive language dataset. We have developed a system with the English language dataset. The results are better when compared to the model we developed in SemEval-2019 Task6.
TheNorth at SemEval-2020 Task 12: Hate Speech Detection Using RoBERTa
Authors: Pedro Alonso, Rajkumar Saini, Gy{\"o}rgy Kovacs
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Hate Speech Detection

Hate speech detection on social media platforms is crucial as it helps to avoid severe situations, and severe harm to marginalized people and groups. The application of Natural Language Processing(NLP) and Deep Learning has garnered encouraging results in the task of hate speech detection. The expression of hate, however is varied and ever evolving. Thus, it is important for better detection systems to adapt to this variance. Because of this, researchers keep on collecting data and regularly come up with hate speech detection competitions. In this paper, we discuss our entry to one such competition, namely the English version of sub-task A for the OffensEval competition. Our contribution can be perceived through our results, which were first a F1-score of 0.9089, and with further refinements described here climb up to0.9166. It serves to give more support to our hypothesis that one of the variants of BERT (Devlin et al., 2018), namely RoBERTa can successfully differentiate between offensive and not-offensive tweets, given some preprocessing steps (also outlined here).
UJNLP at SemEval-2020 Task 12: Detecting Offensive Language Using Bidirectional Transformers
Authors: Yinnan Yao, Nan Su, Kun Ma
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Language Identification, Sentence, Sentence Classification

In this paper, we built several pre-trained models to participate SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media. In the common task of Offensive Language Identification in Social Media, pre-trained models such as Bidirectional Encoder Representation from Transformer (BERT) have achieved good results. We preprocess the dataset by the language habits of users in social network. Considering the data imbalance in OffensEval, we screened the newly provided machine annotation samples to construct a new dataset. We use the dataset to fine-tune the Robustly Optimized BERT Pretraining Approach (RoBERTa). For the English subtask B, we adopted the method of adding Auxiliary Sentences (AS) to transform the single-sentence classification task into a relationship recognition task between sentences. Our team UJNLP wins the ranking 16th of 85 in English subtask A (Offensive language identification).
UNT Linguistics at SemEval-2020 Task 12: Linear SVC with Pre-trained Word Embeddings as Document Vectors and Targeted Linguistic Features
Authors: Jared Fromknecht, Alexis Palmer
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Language Identification, Word Embeddings

This paper outlines our approach to Tasks A {\&} B for the English Language track of SemEval-2020 Task 12: OffensEval 2: Multilingual Offensive Language Identification in Social Media. We use a Linear SVM with document vectors computed from pre-trained word embeddings, and we explore the effectiveness of lexical, part of speech, dependency, and named entity (NE) features. We manually annotate a subset of the training data, which we use for error analysis and to tune a threshold for mapping training confidence values to labels. While document vectors are consistently the most informative features for both tasks, testing on the development set suggests that dependency features are an effective addition for Task A, and NE features for Task B.
UTFPR at SemEval 2020 Task 12: Identifying Offensive Tweets with Lightweight Ensembles
Authors: Marcos Aur{\'e}lio Hermogenes Boriola, Gustavo Henrique Paetzold
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020

Offensive language is a common issue on social media platforms nowadays. In an effort to address this issue, the SemEval 2020 event held the OffensEval 2020 shared task where the participants were challenged to develop systems that identify and classify offensive language in tweets. In this paper, we present a system that uses an Ensemble model stacking a BOW model and a CNN model that led us to place 29th in the ranking for English sub-task A.
YNU\_oxz at SemEval-2020 Task 12: Bidirectional GRU with Capsule for Identifying Multilingual Offensive Language
Authors: Xiaozhi Ou, Hongling Li
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020

This article describes the system submitted to SemEval-2020 Task 12 OffensEval 2: Multilingual Offensive Language Recognition in Social Media. The task is to classify offensive language in social media. The shared task contains five languages (English, Greek, Arabic, Danish, and Turkish) and three subtasks. We only participated in subtask A of English to identify offensive language. To solve this task, we proposed a system based on a Bidirectional Gated Recurrent Unit (Bi-GRU) with a Capsule model. Finally, we used the K-fold approach for ensemble. Our model achieved a Macro-average F1 score of 0.90969 (ranked 27/85) in subtask A.
Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention
Authors: Ningyu Zhang, Shumin Deng, Juan Li, Xi Chen, Wei zhang, Huajun Chen
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020

Online search engines are a popular source of medical information for users, where users can enter questions and obtain relevant answers. It is desirable to generate answer summaries for online search engines, particularly summaries that can reveal direct answers to questions. Moreover, answer summaries are expected to reveal the most relevant information in response to questions; hence, the summaries should be generated with a focus on the question, which is a challenging topic-focused summarization task. In this paper, we propose an approach that utilizes graph convolution networks and question-focused dual attention for Chinese medical answer summarization. We first organize the original long answer text into a medical concept graph with graph convolution networks to better understand the internal structure of the text and the correlation between medical concepts. Then, we introduce a question-focused dual attention mechanism to generate summaries relevant to questions. Experimental results demonstrate that the proposed model can generate more coherent and informative summaries compared with baseline models.
Adversarial Text Generation via Sequence Contrast Discrimination
Authors: Ke Wang, Xiaojun Wan
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Adversarial Text, Text Generation

In this paper, we propose a sequence contrast loss driven text generation framework, which learns the difference between real texts and generated texts and uses that difference. Specifically, our discriminator contains a discriminative sequence generator instead of a binary classifier, and measures the {`}relative realism{'} of generated texts against real texts by making use of them simultaneously. Moreover, our generator uses discriminative sequences to directly improve itself, which not only replaces the gradient propagation process from the discriminator to the generator, but also avoids the time-consuming sampling process of estimating rewards in some previous methods. We conduct extensive experiments with various metrics, substantiating that our framework brings improvements in terms of training stability and the quality of generated texts.
Improving Text Understanding via Deep Syntax-Semantics Communication
Authors: Hao Fei, Yafeng Ren, Donghong Ji
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Sentence

Recent studies show that integrating syntactic tree models with sequential semantic models can bring improved task performance, while these methods mostly employ shallow integration of syntax and semantics. In this paper, we propose a deep neural communication model between syntax and semantics to improve the performance of text understanding. Local communication is performed between syntactic tree encoder and sequential semantic encoder for mutual learning of information exchange. Global communication can further ensure comprehensive information propagation. Results on multiple syntax-dependent tasks show that our model outperforms strong baselines by a large margin. In-depth analysis indicates that our method is highly effective in composing sentence semantics.
A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings
Authors: Katsuhiko Hayashi, Koki Kishimoto, Masashi Shimbo
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Graph Embedding, Knowledge Graph Completion, Knowledge Graph Embedding, Knowledge Graph Embeddings, Quantization

This paper presents a simple and effective discrete optimization method for training binarized knowledge graph embedding model B-CP. Unlike the prior work using a SGD-based method and quantization of real-valued vectors, the proposed method directly optimizes binary embedding vectors by a series of bit flipping operations. On the standard knowledge graph completion tasks, the B-CP model trained with the proposed method achieved comparable performance with that trained with SGD as well as state-of-the-art real-valued models with similar embedding dimensions.
An Attentive Recurrent Model for Incremental Prediction of Sentence-final Verbs
Authors: Wenyan Li, Alvin Grissom II, Jordan Boyd-Graber
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Sentence

Verb prediction is important for understanding human processing of verb-final languages, with practical applications to real-time simultaneous interpretation from verb-final to verb-medial languages. While previous approaches use classical statistical models, we introduce an attention-based neural model to incrementally predict final verbs on incomplete sentences in Japanese and German SOV sentences. To offer flexibility to the model, we further incorporate synonym awareness. Our approach both better predicts the final verbs in Japanese and German and provides more interpretable explanations of why those verbs are selected.
Neural Speed Reading Audited
Authors: Anders S{\o}gaard
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Classification, Document Classification

Several approaches to neural speed reading have been presented at major NLP and machine learning conferences in 2017{--}20; i.e., {``}human-inspired{''} recurrent network architectures that learn to {``}read{''} text faster by skipping irrelevant words, typically optimizing the joint objective of minimizing classification error rate and FLOPs used at inference time. This paper reflects on the meaningfulness of the speed reading task, showing that (a) better and faster approaches to, say, document classification, already exist, which also learn to ignore part of the input (I give an example with 7{\%} error reduction and a 136x speed-up over the state of the art in neural speed reading); and that (b) any claims that neural speed reading is {``}human-inspired{''}, are ill-founded.
Semantic Matching for Sequence-to-Sequence Learning
Authors: Ruiyi Zhang, Changyou Chen, Xinyuan Zhang, Ke Bai, Lawrence Carin
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020

In sequence-to-sequence models, classical optimal transport (OT) can be applied to semantically match generated sentences with target sentences. However, in non-parallel settings, target sentences are usually unavailable. To tackle this issue without losing the benefits of classical OT, we present a semantic matching scheme based on the Optimal Partial Transport (OPT). Specifically, our approach partially matches semantically meaningful words between source and partial target sequences. To overcome the difficulty of detecting active regions in OPT (corresponding to the words needed to be matched), we further exploit prior knowledge to perform partial matching. Extensive experiments are conducted to evaluate the proposed approach, showing consistent improvements over sequence-to-sequence tasks.
Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models
Authors: Zhiyuan Zhang, Xiaoqian Liu, Yi Zhang, Qi Su, Xu sun, Bin He
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Graph Embedding, Knowledge Graph Embedding, World Knowledge

Conventional knowledge graph embedding (KGE) often suffers from limited knowledge representation, leading to performance degradation especially on the low-resource problem. To remedy this, we propose to enrich knowledge representation via pretrained language models by leveraging world knowledge from pretrained models. Specifically, we present a universal training framework named \textit{Pretrain-KGE} consisting of three phases: semantic-based fine-tuning phase, knowledge extracting phase and KGE training phase. Extensive experiments show that our proposed Pretrain-KGE can improve results over KGE models, especially on solving the low-resource problem.
Enhance Robustness of Sequence Labelling with Masked Adversarial Training
Authors: Luoxin Chen, Xinyue Liu, Weitong Ruan, Jianhua Lu
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Chunking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence, text-classification, Text Classification

Adversarial training (AT) has shown strong regularization effects on deep learning algorithms by introducing small input perturbations to improve model robustness. In language tasks, adversarial training brings word-level robustness by adding input noise, which is beneficial for text classification. However, it lacks sufficient contextual information enhancement and thus is less useful for sequence labelling tasks such as chunking and named entity recognition (NER). To address this limitation, we propose masked adversarial training (MAT) to improve robustness from contextual information in sequence labelling. MAT masks or replaces some words in the sentence when computing adversarial loss from perturbed inputs and consequently enhances model robustness using more context-level information. In our experiments, our method shows significant improvements on accuracy and robustness of sequence labelling. By further incorporating with ELMo embeddings, our model achieves better or comparable results to state-of-the-art on CoNLL 2000 and 2003 benchmarks using much less parameters.
The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification
Authors: Abdullatif K{\"o}ksal, Arzucan {\"O}zg{\"u}r
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Classification, Question Answering, Relation, Relation Classification

Relation classification is one of the key topics in information extraction, which can be used to construct knowledge bases or to provide useful information for question answering. Current approaches for relation classification are mainly focused on the English language and require lots of training data with human annotations. Creating and annotating a large amount of training data for low-resource languages is impractical and expensive. To overcome this issue, we propose two cross-lingual relation classification models: a baseline model based on Multilingual BERT and a new multilingual pretraining setup, which significantly improves the baseline with distant supervision. For evaluation, we introduce a new public benchmark dataset for cross-lingual relation classification in English, French, German, Spanish, and Turkish, called RELX. We also provide the RELX-Distant dataset, which includes hundreds of thousands of sentences with relations from Wikipedia and Wikidata collected by distant supervision for these languages. Our code and data are available at: https://github.com/boun-tabi/RELX
Dual Low-Rank Multimodal Fusion
Authors: Tao Jin, Siyu Huang, Yingming Li, Zhongfei Zhang
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020

Tensor-based fusion methods have been proven effective in multimodal fusion tasks. However, existing tensor-based methods make a poor use of the fine-grained temporal dynamics of multimodal sequential features. Motivated by this observation, this paper proposes a novel multimodal fusion method called Fine-Grained Temporal Low-Rank Multimodal Fusion (FT-LMF). FT-LMF correlates the features of individual time steps between multiple modalities, while it involves multiplications of high-order tensors in its calculation. This paper further proposes Dual Low-Rank Multimodal Fusion (Dual-LMF) to reduce the computational complexity of FT-LMF through low-rank tensor approximation along dual dimensions of input features. Dual-LMF is conceptually simple and practically effective and efficient. Empirical studies on benchmark multimodal analysis tasks show that our proposed methods outperform the state-of-the-art tensor-based fusion methods with a similar computational complexity.
A Novel Workflow for Accurately and Efficiently Crowdsourcing Predicate Senses and Argument Labels
Authors: Youxuan Jiang, Huaiyu Zhu, Jonathan K. Kummerfeld, Yunyao Li, Walter Lasecki
Date: 2020-11-01 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Semantic Role Labeling

Resources for Semantic Role Labeling (SRL) are typically annotated by experts at great expense. Prior attempts to develop crowdsourcing methods have either had low accuracy or required substantial expert annotation. We propose a new multi-stage crowd workflow that substantially reduces expert involvement without sacrificing accuracy. In particular, we introduce a unique filter stage based on the key observation that crowd workers are able to almost perfectly filter out incorrect options for labels. Our three-stage workflow produces annotations with 95{\%} accuracy for predicate labels and 93{\%} for argument labels, which is comparable to expert agreement. Compared to prior work on crowdsourcing for SRL, we decrease expert effort by 4x, from 56{\%} to 14{\%} of cases. Our approach enables more scalable annotation of SRL, and could enable annotation of NLP tasks that have previously been considered too complex to effectively crowdsource.