pwc-archive/papers-with-abstracts huggingface dataset:
UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity
Authors: Joe Barrow, Denis Peskov
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Feature Engineering, Machine Translation, Representation Learning, Semantic Similarity, Semantic Textual Similarity, Sentence, STS

We describe a modified shared-LSTM network for the Semantic Textual Similarity (STS) task at SemEval-2017. The network builds on previously explored Siamese network architectures. We treat max sentence length as an additional hyperparameter to be tuned (beyond learning rate, regularization, and dropout). Our results demonstrate that hand-tuning max sentence training length significantly improves final accuracy. After optimizing hyperparameters, we train the network on the multilingual semantic similarity task using pre-translated sentences. We achieved a correlation of 0.4792 for all the subtasks. We achieved the fourth highest team correlation for Task 4b, which was our best relative placement.
MITRE at SemEval-2017 Task 1: Simple Semantic Similarity
Authors: John Henderson, Elizabeth Merkhofer, Laura Strickhart, Guido Zarrella
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: BIG-bench Machine Learning, Image Captioning, Machine Translation, Natural Language Inference, regression, Semantic Similarity, Semantic Textual Similarity, Sentence Embeddings

This paper describes MITRE{'}s participation in the Semantic Textual Similarity task (SemEval-2017 Task 1), which evaluated machine learning approaches to the identification of similar meaning among text snippets in English, Arabic, Spanish, and Turkish. We detail the techniques we explored ranging from simple bag-of-ngrams classifiers to neural architectures with varied attention and alignment mechanisms. Linear regression is used to tie the systems together into an ensemble submitted for evaluation. The resulting system is capable of matching human similarity ratings of image captions with correlations of 0.73 to 0.83 in monolingual settings and 0.68 to 0.78 in cross-lingual conditions, demonstrating the power of relatively simple approaches.
ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity
Authors: Junfeng Tian, Zhiheng Zhou, Man Lan, Yuanbin Wu
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Cross-Lingual Semantic Textual Similarity, Machine Translation, Semantic Similarity, Semantic Textual Similarity

To address semantic similarity on multilingual and cross-lingual sentences, we firstly translate other foreign languages into English, and then feed our monolingual English system with various interactive features. Our system is further supported by combining with deep learning semantic similarity and our best run achieves the mean Pearson correlation 73.16{\%} in primary track.
PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings
Authors: I-Ta Lee, Mahak Goindani, Chang Li, Di Jin, Kristen Marie Johnson, Xiao Zhang, Maria Leonor Pacheco, Dan Goldwasser
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Question Answering, regression, Semantic Textual Similarity, Sentence, STS, Word Embeddings

This paper describes our proposed solution for SemEval 2017 Task 1: Semantic Textual Similarity (Daniel Cer and Specia, 2017). The task aims at measuring the degree of equivalence between sentences given in English. Performance is evaluated by computing Pearson Correlation scores between the predicted scores and human judgements. Our proposed system consists of two subsystems and one regression model for predicting STS scores. The two subsystems are designed to learn Paraphrase and Event Embeddings that can take the consideration of paraphrasing characteristics and sentence structures into our system. The regression model associates these embeddings to make the final predictions. The experimental result shows that our system acquires 0.8 of Pearson Correlation Scores in this task.
RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity
Authors: Ergun Bi{\c{c}}ici
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Machine Translation, Semantic Similarity, Semantic Textual Similarity, STS, Translation

We use referential translation machines for predicting the semantic similarity of text in all STS tasks which contain Arabic, English, Spanish, and Turkish this year. RTMs pioneer a language independent approach to semantic similarity and remove the need to access any task or domain specific information or resource. RTMs become 6th out of 52 submissions in Spanish to English STS. We average prediction scores using weights based on the training performance to improve the overall performance.
LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity
Authors: Ignacio Arroyo-Fern{\'a}ndez, Ivan Vladimir Meza Ruiz
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Embeddings, STS, Word Alignment, Word Embeddings

In this paper we report our attempt to use, on the one hand, state-of-the-art neural approaches that are proposed to measure Semantic Textual Similarity (STS). On the other hand, we propose an unsupervised cross-word alignment approach, which is linguistically motivated. The neural approaches proposed herein are divided into two main stages. The first stage deals with constructing neural word embeddings, the components of sentence embeddings. The second stage deals with constructing a semantic similarity function relating pairs of sentence embeddings. Unfortunately our competition results were poor in all tracks, therefore we concentrated our research to improve them for Track 5 (EN-EN).
L2F/INESC-ID at SemEval-2017 Tasks 1 and 2: Lexical and semantic features in word and textual similarity
Authors: Pedro Fialho, Hugo Patinho Rodrigues, Lu{\'\i}sa Coheur, Paulo Quaresma
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Abstract Meaning Representation, Semantic Textual Similarity, Word Embeddings, Word Similarity

This paper describes our approach to the SemEval-2017 {``}Semantic Textual Similarity{''} and {``}Multilingual Word Similarity{''} tasks. In the former, we test our approach in both English and Spanish, and use a linguistically-rich set of features. These move from lexical to semantic features. In particular, we try to take advantage of the recent Abstract Meaning Representation and SMATCH measure. Although without state of the art results, we introduce semantic structures in textual similarity and analyze their impact. Regarding word similarity, we target the English language and combine WordNet information with Word Embeddings. Without matching the best systems, our approach proved to be simple and effective.
HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity
Authors: Junqing He, Long Wu, Xuemin Zhao, Yonghong Yan
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Cross-Lingual Word Embeddings, Machine Translation, Multilingual Word Embeddings, Semantic Similarity, Semantic Textual Similarity, Sentence, Stock Price Prediction, Task 2, Text Classification, Translation, Transliteration, Word Embeddings, Word Similarity

In this paper, we introduce an approach to combining word embeddings and machine translation for multilingual semantic word similarity, the task2 of SemEval-2017. Thanks to the unsupervised transliteration model, our cross-lingual word embeddings encounter decreased sums of OOVs. Our results are produced using only monolingual Wikipedia corpora and a limited amount of sentence-aligned data. Although relatively little resources are utilized, our system ranked 3rd in the monolingual subtask and can be the 6th in the cross-lingual subtask.
Citius at SemEval-2017 Task 2: Cross-Lingual Similarity from Comparable Corpora and Dependency-Based Contexts
Authors: Pablo Gamallo
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Task 2, Word Similarity

This article describes the distributional strategy submitted by the Citius team to the SemEval 2017 Task 2. Even though the team participated in two subtasks, namely monolingual and cross-lingual word similarity, the article is mainly focused on the cross-lingual subtask. Our method uses comparable corpora and syntactic dependencies to extract count-based and transparent bilingual distributional contexts. The evaluation of the results show that our method is competitive with other cross-lingual strategies, even those using aligned and parallel texts.
Jmp8 at SemEval-2017 Task 2: A simple and general distributional approach to estimate word similarity
Authors: Josu{\'e} Melka, Gilles Bernard
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Dimensionality Reduction, Semantic Similarity, Semantic Textual Similarity, Task 2, Word Similarity

We have built a simple corpus-based system to estimate words similarity in multiple languages with a count-based approach. After training on Wikipedia corpora, our system was evaluated on the multilingual subtask of SemEval-2017 Task 2 and achieved a good level of performance, despite its great simplicity. Our results tend to demonstrate the power of the distributional approach in semantic similarity tasks, even without knowledge of the underlying language. We also show that dimensionality reduction has a considerable impact on the results.
QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base
Authors: Fanqing Meng, Wenpeng Lu, Yuteng Zhang, Ping Jian, Shumin Shi, He-Yan Huang
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Task 2, Word Embeddings, Word Similarity

This paper shows the details of our system submissions in the task 2 of SemEval 2017. We take part in the subtask 1 of this task, which is an English monolingual subtask. This task is designed to evaluate the semantic word similarity of two linguistic items. The results of runs are assessed by standard Pearson and Spearman correlation, contrast with official gold standard set. The best performance of our runs is 0.781 (Final). The techniques of our runs mainly make use of the word embeddings and the knowledge-based method. The results demonstrate that the combined method is effective for the computation of word similarity, while the word embeddings and the knowledge-based technique, respectively, needs more deeply improvement in details.
RUFINO at SemEval-2017 Task 2: Cross-lingual lexical similarity by extending PMI and word embeddings systems with a Swadesh's-like list
Authors: Sergio Jimenez, George Due{\~n}as, Lorena Gaitan, Jorge Segura
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Semantic Textual Similarity, Task 2, Word Embeddings, Word Sense Disambiguation, Word Similarity

The RUFINO team proposed a non-supervised, conceptually-simple and low-cost approach for addressing the Multilingual and Cross-lingual Semantic Word Similarity challenge at SemEval 2017. The proposed systems were cross-lingual extensions of popular monolingual lexical similarity approaches such as PMI and word2vec. The extensions were possible by means of a small parallel list of concepts similar to the Swadesh{'}s list, which we obtained in a semi-automatic way. In spite of its simplicity, our approach showed to be effective obtaining statistically-significant and consistent results in all datasets proposed for the task. Besides, we provide some research directions for improving this novel and affordable approach.
MERALI at SemEval-2017 Task 2 Subtask 1: a Cognitively Inspired approach
Authors: Enrico Mensa, Daniele P. Radicioni, Antonio Lieto
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Common Sense Reasoning, Task 2

In this paper we report on the participation of the MERALI system to the SemEval Task 2 Subtask 1. The MERALI system approaches conceptual similarity through a simple, cognitively inspired, heuristics; it builds on a linguistic resource, the TTCS-e, that relies on BabelNet, NASARI and ConceptNet. The linguistic resource in fact contains a novel mixture of common-sense and encyclopedic knowledge. The obtained results point out that there is ample room for improvement, so that they are used to elaborate on present limitations and on future steps.
HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment
Authors: Behrang QasemiZadeh, Laura Kallmeyer
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Learning Word Embeddings, Semantic Textual Similarity, Task 2, Word Embeddings, Word Similarity

This paper describes the HHU system that participated in Task 2 of SemEval 2017, Multilingual and Cross-lingual Semantic Word Similarity. We introduce our unsupervised embedding learning technique and describe how it was employed and configured to address the problems of monolingual and multilingual word similarity measurement. This paper reports from empirical evaluations on the benchmark provided by the task{'}s organizers.
Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and Knowledge-based Methods to Measure Semantic Word Similarity
Authors: Niloofar Ranjbar, Fatemeh Mashhadirajab, Mehrnoush Shamsfard, Rayeheh Hosseini pour, Aryan Vahid pour
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Semantic Similarity, Semantic Textual Similarity, Task 2, Word Similarity

In this paper, we describe our proposed method for measuring semantic similarity for a given pair of words at SemEval-2017 monolingual semantic word similarity task. We use a combination of knowledge-based and corpus-based techniques. We use FarsNet, the Persian Word Net, besides deep learning techniques to extract the similarity of words. We evaluated our proposed approach on Persian (Farsi) test data at SemEval-2017. It outperformed the other participants and ranked the first in the challenge.
Sew-Embed at SemEval-2017 Task 2: Language-Independent Concept Representations from a Semantically Enriched Wikipedia
Authors: Claudio Delli Bovi, Aless Raganato, ro
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Semantic Textual Similarity, Task 2, Word Similarity

This paper describes Sew-Embed, our language-independent approach to multilingual and cross-lingual semantic word similarity as part of the SemEval-2017 Task 2. We leverage the Wikipedia-based concept representations developed by Raganato et al. (2016), and propose an embedded augmentation of their explicit high-dimensional vectors, which we obtain by plugging in an arbitrary word (or sense) embedding representation, and computing a weighted average in the continuous vector space. We evaluate Sew-Embed with two different off-the-shelf embedding representations, and report their performances across all monolingual and cross-lingual benchmarks available for the task. Despite its simplicity, especially compared with supervised or overly tuned approaches, Sew-Embed achieves competitive results in the cross-lingual setting (3rd best result in the global ranking of subtask 2, score 0.56).
Wild Devs' at SemEval-2017 Task 2: Using Neural Networks to Discover Word Similarity
Authors: R{\u{a}}zvan-Gabriel Rotari, Ionu{\textcommabelow{t}} Hulub, {\textcommabelow{S}}tefan Oprea, Mihaela Pl{\u{a}}mad{\u{a}}-Onofrei, Alina Beatrice Loren{\c{t}}, Raluca Preisler, Adrian Iftene, Tr, Diana ab{\u{a}}{\textcommabelow{t}}
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Multilingual Word Embeddings, Semantic Similarity, Semantic Textual Similarity, Task 2, Word Embeddings, Word Similarity

This paper presents Wild Devs{'} participation in the SemEval-2017 Task 2 {``}Multi-lingual and Cross-lingual Semantic Word Similarity{''}, which tries to automatically measure the semantic similarity between two words. The system was build using neural networks, having as input a collection of word pairs, whereas the output consists of a list of scores, from 0 to 4, corresponding to the degree of similarity between the word pairs.
TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers
Authors: Mohammed R. H. Qwaider, Abed Alhakim Freihat, Fausto Giunchiglia
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: BIG-bench Machine Learning, Named Entity Recognition (NER), Sentiment Analysis

In this paper we present the Tren-toTeam system which participated to thetask 3 at SemEval-2017 (Nakov et al.,2017).We concentrated our work onapplying Grice Maxims(used in manystate-of-the-art Machine learning applica-tions(Vogel et al., 2013; Kheirabadiand Aghagolzadeh, 2012; Dale and Re-iter, 1995; Franke, 2011)) to ranking an-swers of a question by answers relevancy.Particularly, we created a ranker systembased on relevancy scores, assigned by 3main components: Named entity recogni-tion, similarity score, sentiment analysis.Our system obtained a comparable resultsto Machine learning systems.
UPC-USMBA at SemEval-2017 Task 3: Combining multiple approaches for CQA for Arabic
Authors: Yassine El Adlouni, Imane Lahbari, Horacio Rodr{\'\i}guez, Mohammed Meknassi, Said Ouatik El Alaoui, Noureddine Ennahnahi
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Question Answering

This paper presents a description of the participation of the UPC-USMBA team in the SemEval 2017 Task 3, subtask D, Arabic. Our approach for facing the task is based on a combination of a set of atomic classifiers. The atomic classifiers include lexical string based, based on vectorial representations and rulebased. Several combination approaches have been tried.
Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering
Authors: Wenzheng Feng, Yu Wu, Wei Wu, Zhoujun Li, Ming Zhou
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Answer Selection, Community Question Answering, Learning-To-Rank, Question Answering, Question Similarity, Retrieval, text similarity

This paper presents the system in SemEval-2017 Task 3, Community Question Answering (CQA). We develop a ranking system that is capable of capturing semantic relations between text pairs with little word overlap. In addition to traditional NLP features, we introduce several neural network based matching features which enable our system to measure text similarity beyond lexicons. Our system significantly outperforms baseline methods and holds the second place in Subtask A and the fifth place in Subtask B, which demonstrates its efficacy on answer selection and question retrieval.
MoRS at SemEval-2017 Task 3: Easy to use SVM in Ranking Tasks
Authors: Miguel J. Rodrigues, Francisco M. Couto
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Community Question Answering, Information Retrieval, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), POS, POS Tagging, Question Answering, Retrieval

This paper describes our system, dubbed MoRS (Modular Ranking System), pronounced {`}Morse{'}, which participated in Task 3 of SemEval-2017. We used MoRS to perform the Community Question Answering Task 3, which consisted on reordering a set of comments according to their usefulness in answering the question in the thread. This was made for a large collection of questions created by a user community. As for this challenge we wanted to go back to simple, easy-to-use, and somewhat forgotten technologies that we think, in the hands of non-expert people, could be reused in their own data sets. Some of our techniques included the annotation of text, the retrieval of meta-data for each comment, POS tagging and Named Entity Recognition, among others. These gave place to syntactical analysis and semantic measurements. Finally we show and discuss our results and the context of our approach, which is part of a more comprehensive system in development, named MoQA.
EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering
Authors: Yufei Xie, Maoquan Wang, Jing Ma, Jian Jiang, Zhao Lu
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Community Question Answering, Feature Engineering, Question Answering, Question Similarity

We describe our system for participating in SemEval-2017 Task 3 on Community Question Answering. Our approach relies on combining a rich set of various types of features: semantic and metadata. The most important group turned out to be the metadata feature and the semantic vectors trained on QatarLiving data. In the main Subtask C, our primary submission was ranked fourth, with a MAP of 13.48 and accuracy of 97.08. In Subtask A, our primary submission get into the top 50{\%}.
FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering
Authors: Giuseppe Attardi, Antonio Carta, Federico Errica, Andrea Madotto, Ludovica Pannitto
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Community Question Answering, Dependency Parsing, Model Selection, Question Answering, Question Similarity, Word Embeddings

In this paper we present ThReeNN, a model for Community Question Answering, Task 3, of SemEval-2017. The proposed model exploits both syntactic and semantic information to build a single and meaningful embedding space. Using a dependency parser in combination with word embeddings, the model creates sequences of inputs for a Recurrent Neural Network, which are then used for the ranking purposes of the Task. The score obtained on the official test data shows promising results.
SCIR-QA at SemEval-2017 Task 3: CNN Model Based on Similar and Dissimilar Information between Keywords for Question Similarity
Authors: Le Qi, Yu Zhang, Ting Liu
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Community Question Answering, Graph Ranking, Information Retrieval, Keyword Extraction, Question Answering, Question Similarity

We describe a method of calculating the similarity of questions in community QA. Question in cQA are usually very long and there are a lot of useless information about calculating the similarity of questions. Therefore,we implement a CNN model based on similar and dissimilar information between question{'}s keywords. We extract the keywords of questions, and then model the similar and dissimilar information between the keywords, and use the CNN model to calculate the similarity.
LearningToQuestion at SemEval 2017 Task 3: Ranking Similar Questions by Learning to Rank Using Rich Features
Authors: Naman Goyal
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Information Retrieval, Learning-To-Rank, Question Answering, Representation Learning

This paper describes our official entry LearningToQuestion for SemEval 2017 task 3 community question answer, subtask B. The objective is to rerank questions obtained in web forum as per their similarity to original question. Our system uses pairwise learning to rank methods on rich set of hand designed and representation learning features. We use various semantic features that help our system to achieve promising results on the task. The system achieved second highest results on official metrics MAP and good results on other search metrics.