pwc-archive/papers-with-abstracts huggingface dataset:
ELiRF-UPV at SemEval-2018 Task 10: Capturing Discriminative Attributes with Knowledge Graphs and Wikipedia
Authors: Jos{\'e}-{\'A}ngel Gonz{\'a}lez, Llu{\'\i}s-F. Hurtado, Encarna Segarra, Ferran Pla
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Knowledge Graphs, Relation Extraction

This paper describes the participation of ELiRF-UPV team at task 10, Capturing Discriminative Attributes, of SemEval-2018. Our best approach consists of using ConceptNet, Wikipedia and NumberBatch embeddings in order to stablish relationships between concepts and attributes. Furthermore, this system achieves competitive results in the official evaluation.
Wolves at SemEval-2018 Task 10: Semantic Discrimination based on Knowledge and Association
Authors: Shiva Taslimipoor, Omid Rohanian, Le An Ha, Gloria Corpas Pastor, Ruslan Mitkov
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Attribute, Relation, Semantic Textual Similarity, Word Similarity

This paper describes the system submitted to SemEval 2018 shared task 10 {`}Capturing Dicriminative Attributes{'}. We use a combination of knowledge-based and co-occurrence features to capture the semantic difference between two words in relation to an attribute. We define scores based on association measures, ngram counts, word similarity, and ConceptNet relations. The system is ranked 4th (joint) on the official leaderboard of the task.
UNAM at SemEval-2018 Task 10: Unsupervised Semantic Discriminative Attribute Identification in Neural Word Embedding Cones
Authors: Ignacio Arroyo-Fern{\'a}ndez, Ivan Meza, Carlos-Francisco M{\'e}ndez-Cruz
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Attribute, Word Embeddings

In this paper we report an unsupervised method aimed to identify whether an attribute is discriminative for two words (which are treated as concepts, in our particular case). To this end, we use geometrically inspired vector operations underlying unsupervised decision functions. These decision functions operate on state-of-the-art neural word embeddings of the attribute and the concepts. The main idea can be described as follows: if attribute $q$ discriminates concept $a$ from concept $b$, then $q$ is excluded from the feature set shared by these two concepts: the intersection. That is, the membership $q\in (a\cap b)$ does not hold. As $a,b,q$ are represented with neural word embeddings, we tested vector operations allowing us to measure membership, i.e. fuzzy set operations (t-norm, for fuzzy intersection, and t-conorm, for fuzzy union) and the similarity between $q$ and the convex cone described by $a$ and $b$.
Igevorse at SemEval-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative Attributes
Authors: Maxim Grishin
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: BIG-bench Machine Learning, General Classification, Semantic Textual Similarity, Word Embeddings

This paper presents a comparison of several approaches for capturing discriminative attributes and considers an impact of concatenation of several word embeddings of different nature on the classification performance. A similarity-based method is proposed and compared with classical machine learning approaches. It is shown that this method outperforms others on all the considered word vector models and there is a performance increase when concatenated datasets are used.
ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection
Authors: Yunxiao Zhou, Man Lan, Yuanbin Wu
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Attribute, BIG-bench Machine Learning, Feature Engineering, Machine Translation, Semantic Textual Similarity, Word Embeddings

This paper describes the system we submitted to Task 10 (Capturing Discriminative Attributes) in SemEval 2018. Given a triple (word1, word2, attribute), this task is to predict whether it exemplifies a semantic difference or not. We design and investigate several word embedding features, PMI features and WordNet features together with supervised machine learning methods to address this task. Officially released results show that our system ranks above average.
AmritaNLP at SemEval-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation.
Authors: Vivek Vinayan, An Kumar M, Soman K. P
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6

The {``}Capturing Discriminative Attributes{''} sharedtask is the tenth task, conjoint with SemEval2018. The task is to predict if a word can capture distinguishing attributes of one word from another. We use GloVe word embedding, pre-trained on openly sourced corpus for this task. A base representation is initially established over varied dimensions. These representations are evaluated based on validation scores over two models, first on an SVM based classifier and second on a one dimension CNN model. The scores are used to further develop the representation with vector combinations, by considering various distance measures. These measures correspond to offset vectors which are concatenated as features, mainly to improve upon the F1score, with the best accuracy. The features are then further tuned on the validation scores, to achieve highest F1score. Our evaluation narrowed down to two representations, classified on CNN models, having a total dimension length of 1204 {\&} 1203 for the final submissions. Of the two, the latter feature representation delivered our best F1score of 0.658024 (as per result).
Discriminator at SemEval-2018 Task 10: Minimally Supervised Discrimination
Authors: Artur Kulmizev, Mostafa Abdou, Vinit Ravishankar, Malvina Nissim
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6

We participated to the SemEval-2018 shared task on capturing discriminative attributes (Task 10) with a simple system that ranked 8th amongst the 26 teams that took part in the evaluation. Our final score was 0.67, which is competitive with the winning score of 0.75, particularly given that our system is a zero-shot system that requires no training and minimal parameter optimisation. In addition to describing the submitted system, and discussing the implications of the relative success of such a system on this task, we also report on other, more complex models we experimented with.
UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes
Authors: Milton King, Ali Hakimi Parizi, Paul Cook
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Semantic Textual Similarity, Sentence, Word Embeddings

In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these approaches, the simple approach based on word co-occurrence performs best. We further consider supervised and unsupervised approaches to combining information from these models, but these approaches do not improve on the word co-occurrence model.
ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet
Authors: Rui Mao, Guanyi Chen, Ruizhe Li, Chenghua Lin
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Attribute, Semantic Textual Similarity, Word Embeddings

This paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes. Our system is built upon a simple idea of measuring the attribute word{'}s similarity with each of the two semantically similar words, based on an extended word embedding method and WordNet. Instead of computing the similarities between the attribute and semantically similar words by using standard word embeddings, we propose a novel method that combines word and context embeddings which can better measure similarities. Our model is simple and effective, which achieves an average F1 score of 0.62 on the test set.
UMD at SemEval-2018 Task 10: Can Word Embeddings Capture Discriminative Attributes?
Authors: Alex Zhang, er, Marine Carpuat
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Attribute, Binary Classification, Semantic Textual Similarity, Word Embeddings

We describe the University of Maryland{'}s submission to SemEval-018 Task 10, {``}Capturing Discriminative Attributes{''}: given word triples (w1, w2, d), the goal is to determine whether d is a discriminating attribute belonging to w1 but not w2. Our study aims to determine whether word embeddings can address this challenging task. Our submission casts this problem as supervised binary classification using only word embedding features. Using a gaussian SVM model trained only on validation data results in an F-score of 60{\%}. We also show that cosine similarity features are more effective, both in unsupervised systems (F-score of 65{\%}) and supervised systems (F-score of 67{\%}).
NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge
Authors: Yow-Ting Shiue, Hen-Hsen Huang, Hsin-Hsi Chen
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Attribute, Machine Translation, Semantic Textual Similarity, Sentiment Analysis, Word Embeddings

This paper presents the NTU NLP Lab system for the SemEval-2018 Capturing Discriminative Attributes task. Word embeddings, pointwise mutual information (PMI), ConceptNet edges and shortest path lengths are utilized as input features to build binary classifiers to tell whether an attribute is discriminative for a pair of concepts. Our neural network model reaches about 73{\%} F1 score on the test set and ranks the 3rd in the task. Though the attributes to deal with in this task are all visual, our models are not provided with any image data. The results indicate that visual information can be derived from textual data.
ELiRF-UPV at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge
Authors: Jos{\'e}-{\'A}ngel Gonz{\'a}lez, Llu{\'\i}s-F. Hurtado, Encarna Segarra, Ferran Pla
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Multiple-choice, Question Answering, Reading Comprehension, Word Embeddings

This paper describes the participation of ELiRF-UPV team at task 11, Machine Comprehension using Commonsense Knowledge, of SemEval-2018. Our approach is based on the use of word embeddings, NumberBatch Embeddings, and a Deep Learning architecture to find the best answer for the multiple-choice questions based on the narrative text. The results obtained are in line with those obtained by the other participants and they encourage us to continue working on this problem.
YNU\_AI1799 at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble
Authors: Qingxun Liu, Hongdou Yao, Xaobing Zhou, Ge Xie
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Language Modeling, Language Modelling, Machine Reading Comprehension, Position, Reading Comprehension, Text Classification

In this paper, we describe a machine reading comprehension system that participated in SemEval-2018 Task 11: Machine Comprehension using commonsense knowledge. In this work, we train a series of neural network models such as multi-LSTM, BiLSTM, multi- BiLSTM-CNN and attention-based BiLSTM, etc. On top of some sub models, there are two kinds of word embedding: (a) general word embedding generated from unsupervised neural language model; and (b) position embedding generated from general word embedding. Finally, we make a hard vote on the predictions of these models and achieve relatively good result. The proposed approach achieves 8th place in Task 11 with the accuracy of 0.7213.
YNU\_Deep at SemEval-2018 Task 11: An Ensemble of Attention-based BiLSTM Models for Machine Comprehension
Authors: Peng Ding, Xiaobing Zhou
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Machine Translation, Reading Comprehension, Text Summarization

We firstly use GloVe to learn the distributed representations automatically from the instance, question and answer triples. Then an attentionbased Bidirectional LSTM (BiLSTM) model is used to encode the triples. We also perform a simple ensemble method to improve the effectiveness of our model. The system we developed obtains an encouraging result on this task. It achieves the accuracy 0.7472 on the test set. We rank 5th according to the official ranking.
ECNU at SemEval-2018 Task 11: Using Deep Learning Method to Address Machine Comprehension Task
Authors: Yixuan Sheng, Man Lan, Yuanbin Wu
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Machine Reading Comprehension, Reading Comprehension

This paper describes the system we submitted to the Task 11 in SemEval 2018, i.e., Machine Comprehension using Commonsense Knowledge. Given a passage and some questions that each have two candidate answers, this task requires the participate system to select out one answer meet the meaning of original text or commonsense knowledge from the candidate answers. For this task, we use a deep learning method to obtain final predict answer by calculating relevance of choices representations and question-aware document representation.
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment
Authors: Zhengping Jiang, Qi Sun
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Common Sense Reasoning, Language Modelling, Machine Reading Comprehension, Multiple-choice, Natural Language Inference, Question Answering, Reading Comprehension, Word Embeddings

In this document we present an end-to-end machine reading comprehension system that solves multiple choice questions with a textual entailment perspective. Since some of the knowledge required is not explicitly mentioned in the text, we try to exploit commonsense knowledge by using pretrained word embeddings during contextual embeddings and by dynamically generating a weighted representation of related script knowledge. In the model two kinds of prediction structure are ensembled, and the final accuracy of our system is 10 percent higher than the naiive baseline.
YNU-HPCC at Semeval-2018 Task 11: Using an Attention-based CNN-LSTM for Machine Comprehension using Commonsense Knowledge
Authors: Hang Yuan, Jin Wang, Xue-jie Zhang
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Question Answering, Reading Comprehension

This shared task is a typical question answering task. Compared with the normal question and answer system, it needs to give the answer to the question based on the text provided. The essence of the problem is actually reading comprehension. Typically, there are several questions for each text that correspond to it. And for each question, there are two candidate answers (and only one of them is correct). To solve this problem, the usual approach is to use convolutional neural networks (CNN) and recurrent neural network (RNN) or their improved models (such as long short-term memory (LSTM)). In this paper, an attention-based CNN-LSTM model is proposed for this task. By adding an attention mechanism and combining the two models, this experimental result has been significantly improved.
Jiangnan at SemEval-2018 Task 11: Deep Neural Network with Attention Method for Machine Comprehension Task
Authors: Jiangnan Xia
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Machine Reading Comprehension, Named Entity Recognition (NER), Question Answering, Reading Comprehension

This paper describes our submission for the International Workshop on Semantic Evaluation (SemEval-2018) shared task 11{--} Machine Comprehension using Commonsense Knowledge (Ostermann et al., 2018b). We use a deep neural network model to choose the correct answer from the candidate answers pair when the document and question are given. The interactions between document, question and answers are modeled by attention mechanism and a variety of manual features are used to improve model performance. We also use CoVe (McCann et al., 2017) as an external source of knowledge which is not mentioned in the document. As a result, our system achieves 80.91{\%} accuracy on the test data, which is on the third place of the leaderboard.
IUCM at SemEval-2018 Task 11: Similar-Topic Texts as a Comprehension Knowledge Source
Authors: Sofia Reznikova, Leon Derczynski
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Clustering, Lemmatization, Question Answering, Reading Comprehension

This paper describes the IUCM entry at SemEval-2018 Task 11, on machine comprehension using commonsense knowledge. First, clustering and topic modeling are used to divide given texts into topics. Then, during the answering phase, other texts of the same topic are retrieved and used as commonsense knowledge. Finally, the answer is selected. While clustering itself shows good results, finding an answer proves to be more challenging. This paper reports the results of system evaluation and suggests potential improvements.
Lyb3b at SemEval-2018 Task 11: Machine Comprehension Task using Deep Learning Models
Authors: Yongbin Li, Xiaobing Zhou
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Data Augmentation, Reading Comprehension

Machine Comprehension of text is a typical Natural Language Processing task which remains an elusive challenge. This paper is to solve the task 11 of SemEval-2018, Machine Comprehension using Commonsense Knowledge task. We use deep learning model to solve the problem. We build distributed word embedding of text, question and answering respectively instead of manually extracting features by linguistic tools. Meanwhile, we use a series of frameworks such as CNN model, LSTM model, LSTM with attention model and biLSTM with attention model for processing word vector. Experiments demonstrate the superior performance of biLSTM with attention framework compared to other models. We also delete high frequency words and combine word vector and data augmentation methods, achieved a certain effect. The approach we proposed rank 6th in official results, with accuracy rate of 0.7437 in test dataset.
MITRE at SemEval-2018 Task 11: Commonsense Reasoning without Commonsense Knowledge
Authors: Elizabeth Merkhofer, John Henderson, David Bloom, Laura Strickhart, Guido Zarrella
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Common Sense Reasoning, Information Retrieval, Reading Comprehension, regression

This paper describes MITRE{'}s participation in SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. The techniques explored range from simple bag-of-ngrams classifiers to neural architectures with varied attention and alignment mechanisms. Logistic regression ties the systems together into an ensemble submitted for evaluation. The resulting system answers reading comprehension questions with 82.27{\%} accuracy.
SNU\_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension
Authors: Taeuk Kim, Jihun Choi, Sang-goo Lee
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Machine Translation, Sentence, Transfer Learning, Translation

We present a novel neural architecture for the Argument Reasoning Comprehension task of SemEval 2018. It is a simple neural network consisting of three parts, collectively judging whether the logic built on a set of given sentences (a claim, reason, and warrant) is plausible or not. The model utilizes contextualized word vectors pre-trained on large machine translation (MT) datasets as a form of transfer learning, which can help to mitigate the lack of training data. Quantitative analysis shows that simply leveraging LSTMs trained on MT datasets outperforms several baselines and non-transferred models, achieving accuracies of about 70{\%} on the development set and about 60{\%} on the test set.
ITNLP-ARC at SemEval-2018 Task 12: Argument Reasoning Comprehension with Attention
Authors: Wenjie Liu, Chengjie Sun, Lei Lin, Bingquan Liu
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: ARC, Information Retrieval, Machine Translation, Natural Language Inference, Relation Extraction, Semantic Textual Similarity, Sentence, Text Summarization

Reasoning is a very important topic and has many important applications in the field of natural language processing. Semantic Evaluation (SemEval) 2018 Task 12 {``}The Argument Reasoning Comprehension{''} committed to research natural language reasoning. In this task, we proposed a novel argument reasoning comprehension system, ITNLP-ARC, which use Neural Networks technology to solve this problem. In our system, the LSTM model is involved to encode both the premise sentences and the warrant sentences. The attention model is used to merge the two premise sentence vectors. Through comparing the similarity between the attention vector and each of the two warrant vectors, we choose the one with higher similarity as our system{'}s final answer.
ECNU at SemEval-2018 Task 12: An End-to-End Attention-based Neural Network for the Argument Reasoning Comprehension Task
Authors: Junfeng Tian, Man Lan, Yuanbin Wu
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6

This paper presents our submissions to SemEval 2018 Task 12: the Argument Reasoning Comprehension Task. We investigate an end-to-end attention-based neural network to represent the two lexically close candidate warrants. On the one hand, we extract their different parts as attention vectors to obtain distinguishable representations. On the other hand, we use their surrounds (i.e., claim, reason, debate context) as another attention vectors to get contextual representations, which work as final clues to select the correct warrant. Our model achieves 60.4{\%} accuracy and ranks 3rd among 22 participating systems.
BLCU\_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention
Authors: Meiqian Zhao, Chunhua Liu, Lu Liu, Yan Zhao, Dong Yu
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Common Sense Reasoning, Word Embeddings

To comprehend an argument and fill the gap between claims and reasons, it is vital to find the implicit supporting warrants behind. In this paper, we propose a hierarchical attention model to identify the right warrant which explains why the reason stands for the claim. Our model focuses not only on the similar part between warrants and other information but also on the contradictory part between two opposing warrants. In addition, we use the ensemble method for different models. Our model achieves an accuracy of 61{\%}, ranking second in this task. Experimental results demonstrate that our model is effective to make correct choices.
YNU-HPCC at SemEval-2018 Task 12: The Argument Reasoning Comprehension Task Using a Bi-directional LSTM with Attention Model
Authors: Quanlei Liao, Xutao Yang, Jin Wang, Xue-jie Zhang
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Ensemble Learning, Question Answering

An argument is divided into two parts, the claim and the reason. To obtain a clearer conclusion, some additional explanation is required. In this task, the explanations are called warrants. This paper introduces a bi-directional long short term memory (Bi-LSTM) with an attention model to select a correct warrant from two to explain an argument. We address this question as a question-answering system. For each warrant, the model produces a probability that it is correct. Finally, the system chooses the highest correct probability as the answer. Ensemble learning is used to enhance the performance of the model. Among all of the participants, we ranked 15th on the test results.