pwc-archive/papers-with-abstracts huggingface dataset:
SYSTRAN @ WAT 2019: Russian-Japanese News Commentary task
Authors: Jitao Xu, TuAnh Nguyen, MinhQuang Pham, Josep Crego, Jean Senellart
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Data Augmentation, Synthetic Data Generation, Translation

This paper describes Systran{'}s submissions to WAT 2019 Russian-Japanese News Commentary task. A challenging translation task due to the extremely low resources available and the distance of the language pair. We have used the neural Transformer architecture learned over the provided resources and we carried out synthetic data generation experiments which aim at alleviating the data scarcity problem. Results indicate the suitability of the data augmentation experiments, enabling our systems to rank first according to automatic evaluations.
UCSYNLP-Lab Machine Translation Systems for WAT 2019
Authors: Yimon ShweSin, Win Pa Pa, KhinMar Soe
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, NMT, Segmentation, Translation

This paper describes the UCSYNLP-Lab submission to WAT 2019 for Myanmar-English translation tasks in both direction. We have used the neural machine translation systems with attention model and utilized the UCSY-corpus and ALT corpus. In NMT with attention model, we use the word segmentation level as well as syllable segmentation level. Especially, we made the UCSY-corpus to be cleaned in WAT 2019. Therefore, the UCSY corpus for WAT 2019 is not identical to those used in WAT 2018. Experiments show that the translation systems can produce the substantial improvements.
Sentiment Aware Neural Machine Translation
Authors: Chenglei Si, Kui Wu, Ai Ti Aw, Min-Yen Kan
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, NMT, Sentence, Translation, Word Translation

Sentiment ambiguous lexicons refer to words where their polarity depends strongly on con- text. As such, when the context is absent, their translations or their embedded sentence ends up (incorrectly) being dependent on the training data. While neural machine translation (NMT) has achieved great progress in recent years, most systems aim to produce one single correct translation for a given source sentence. We investigate the translation variation in two sentiment scenarios. We perform experiments to study the preservation of sentiment during translation with three different methods that we propose. We conducted tests with both sentiment and non-sentiment bearing contexts to examine the effectiveness of our methods. We show that NMT can generate both positive- and negative-valent translations of a source sentence, based on a given input sentiment label. Empirical evaluations show that our valence-sensitive embedding (VSE) method significantly outperforms a sequence-to-sequence (seq2seq) baseline, both in terms of BLEU score and ambiguous word translation accuracy in test, given non-sentiment bearing contexts.
Relation Prediction for Unseen-Entities Using Entity-Word Graphs
Authors: Yuki Tagawa, Motoki Taniguchi, Yasuhide Miura, Tomoki Taniguchi, Tomoko Ohkuma, Takayuki Yamamoto, Keiichi Nemoto
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Knowledge Graph Completion, Knowledge Graphs, Relation, Relation Prediction

Knowledge graphs (KGs) are generally used for various NLP tasks. However, as KGs still miss some information, it is necessary to develop Knowledge Graph Completion (KGC) methods. Most KGC researches do not focus on the Out-of-KGs entities (Unseen-entities), we need a method that can predict the relation for the entity pairs containing Unseen-entities to automatically add new entities to the KGs. In this study, we focus on relation prediction and propose a method to learn entity representations via a graph structure that uses Seen-entities, Unseen-entities and words as nodes created from the descriptions of all entities. In the experiments, our method shows a significant improvement in the relation prediction for the entity pairs containing Unseen-entities.
Scalable graph-based method for individual named entity identification
Authors: Sammy Khalife, Michalis Vazirgiannis
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Entity Linking

In this paper, we consider the named entity linking (NEL) problem. We assume a set of queries, named entities, that have to be identified within a knowledge base. This knowledge base is represented by a text database paired with a semantic graph, endowed with a classification of entities (ontology). We present state-of-the-art methods in NEL, and propose a new method for individual identification requiring few annotated data samples. We demonstrate its scalability and performance over standard datasets, for several ontology configurations. Our approach is well-motivated for integration in real systems. Indeed, recent deep learning methods, despite their capacity to improve experimental precision, require lots of parameter tuning along with large volume of annotated data.
Neural Speech Translation using Lattice Transformations and Graph Networks
Authors: Daniel Beck, Trevor Cohn, Gholamreza Haffari
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Translation

Speech translation systems usually follow a pipeline approach, using word lattices as an intermediate representation. However, previous work assume access to the original transcriptions used to train the ASR system, which can limit applicability in real scenarios. In this work we propose an approach for speech translation through lattice transformations and neural models based on graph networks. Experimental results show that our approach reaches competitive performance without relying on transcriptions, while also being orders of magnitude faster than previous work.
Using Graphs for Word Embedding with Enhanced Semantic Relations
Authors: Matan Zuckerman, Mark Last
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: text-classification, Text Classification

Word embedding algorithms have become a common tool in the field of natural language processing. While some, like Word2Vec, are based on sequential text input, others are utilizing a graph representation of text. In this paper, we introduce a new algorithm, named WordGraph2Vec, or in short WG2V, which combines the two approaches to gain the benefits of both. The algorithm uses a directed word graph to provide additional information for sequential text input algorithms. Our experiments on benchmark datasets show that text classification algorithms are nearly as accurate with WG2V as with other word embedding models while preserving more stable accuracy rankings.
TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration
Authors: Peter Jansen, Dmitry Ustalov
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Information Retrieval, Question Answering, Retrieval, World Knowledge

While automated question answering systems are increasingly able to retrieve answers to natural language questions, their ability to generate detailed human-readable explanations for their answers is still quite limited. The Shared Task on Multi-Hop Inference for Explanation Regeneration tasks participants with regenerating detailed gold explanations for standardized elementary science exam questions by selecting facts from a knowledge base of semi-structured tables. Each explanation contains between 1 and 16 interconnected facts that form an {``}explanation graph{''} spanning core scientific knowledge and detailed world knowledge. It is expected that successfully combining these facts to generate detailed explanations will require advancing methods in multi-hop inference and information combination, and will make use of the supervised training data provided by the WorldTree explanation corpus. The top-performing system achieved a mean average precision (MAP) of 0.56, substantially advancing the state-of-the-art over a baseline information retrieval model. Detailed extended analyses of all submitted systems showed large relative improvements in accessing the most challenging multi-hop inference problems, while absolute performance remains low, highlighting the difficulty of generating detailed explanations through multi-hop reasoning.
Team SVMrank: Leveraging Feature-rich Support Vector Machines for Ranking Explanations to Elementary Science Questions
Authors: Jennifer D{'}Souza, Isaiah On Mulang{'}, o, S{\"o}ren Auer
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: AI2 Reasoning Challenge, ARC, Explanation Generation, Learning-To-Rank, Question Answering, Reranking

The TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration (MIER-19) tackles explanation generation for answers to elementary science questions. It builds on the AI2 Reasoning Challenge 2018 (ARC-18) which was organized as an advanced question answering task on a dataset of elementary science questions. The ARC-18 questions were shown to be hard to answer with systems focusing on surface-level cues alone, instead requiring far more powerful knowledge and reasoning. To address MIER-19, we adopt a hybrid pipelined architecture comprising a featurerich learning-to-rank (LTR) machine learning model, followed by a rule-based system for reranking the LTR model predictions. Our system was ranked fourth in the official evaluation, scoring close to the second and third ranked teams, achieving 39.4{\%} MAP.
Chains-of-Reasoning at TextGraphs 2019 Shared Task: Reasoning over Chains of Facts for Explainable Multi-hop Inference
Authors: Rajarshi Das, Ameya Godbole, Manzil Zaheer, Shehzaad Dhuliawala, Andrew McCallum
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11

This paper describes our submission to the shared task on {``}Multi-hop Inference Explanation Regeneration{''} in TextGraphs workshop at EMNLP 2019 (Jansen and Ustalov, 2019). Our system identifies chains of facts relevant to explain an answer to an elementary science examination question. To counter the problem of {`}spurious chains{'} leading to {`}semantic drifts{'}, we train a ranker that uses contextualized representation of facts to score its relevance for explaining an answer to a question. Our system was ranked first w.r.t the mean average precision (MAP) metric outperforming the second best system by 14.95 points.
Joint Semantic and Distributional Word Representations with Multi-Graph Embeddings
Authors: Pierre Daix-Moreux, Matthias Gall{\'e}
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Word Embeddings

Word embeddings continue to be of great use for NLP researchers and practitioners due to their training speed and easiness of use and distribution. Prior work has shown that the representation of those words can be improved by the use of semantic knowledge-bases. In this paper we propose a novel way of combining those knowledge-bases while the lexical information of co-occurrences of words remains. It is conceptually clear, as it consists in mapping both distributional and semantic information into a multi-graph and modifying existing node embeddings techniques to compute word representations. Our experiments show improved results compared to vanilla word embeddings, retrofitting and concatenation techniques using the same information, on a variety of data-sets of word similarities.
Evaluating Research Novelty Detection: Counterfactual Approaches
Authors: Reinald Kim Amplayo, Seung-won Hwang, Min Song
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: counterfactual, feature selection, Novelty Detection

In this paper, we explore strategies to evaluate models for the task research paper novelty detection: Given all papers released at a given date, which of the papers discuss new ideas and influence future research? We find the novelty is not a singular concept, and thus inherently lacks of ground truth annotations with cross-annotator agreement, which is a major obstacle in evaluating these models. Test-of-time award is closest to such annotation, which can only be made retrospectively and is extremely scarce. We thus propose to compare and evaluate models using counterfactual simulations. First, we ask models if they can differentiate papers at time $t$ and counterfactual paper from future time $t+d$. Second, we ask models if they can predict test-of-time award at $t+d$. These are proxies that can be agreed by human annotators and easily augmented by correlated signals, using which evaluation can be done through four tasks: classification, ranking, correlation and feature selection. We show these proxy evaluation methods complement each other regarding error handling, coverage, interpretability, and scope, and thus altogether contribute to the observation of the relative strength of existing models.
Faceted Hierarchy: A New Graph Type to Organize Scientific Concepts and a Construction Method
Authors: Qingkai Zeng, Mengxia Yu, Wenhao Yu, JinJun Xiong, Yiyu Shi, Meng Jiang
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Face Recognition

On a scientific concept hierarchy, a parent concept may have a few attributes, each of which has multiple values being a group of child concepts. We call these attributes facets: classification has a few facets such as application (e.g., face recognition), model (e.g., svm, knn), and metric (e.g., precision). In this work, we aim at building faceted concept hierarchies from scientific literature. Hierarchy construction methods heavily rely on hypernym detection, however, the faceted relations are parent-to-child links but the hypernym relation is a multi-hop, i.e., ancestor-to-descendent link with a specific facet {``}type-of{''}. We use information extraction techniques to find synonyms, sibling concepts, and ancestor-descendent relations from a data science corpus. And we propose a hierarchy growth algorithm to infer the parent-child links from the three types of relationships. It resolves conflicts by maintaining the acyclic structure of a hierarchy.
Graph-Based Semi-Supervised Learning for Natural Language Understanding
Authors: Zimeng Qiu, Eunah Cho, Xiaochun Ma, William Campbell
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Natural Language Understanding

Semi-supervised learning is an efficient method to augment training data automatically from unlabeled data. Development of many natural language understanding (NLU) applications has a challenge where unlabeled data is relatively abundant while labeled data is rather limited. In this work, we propose transductive graph-based semi-supervised learning models as well as their inductive variants for NLU. We evaluate the approach{'}s applicability using publicly available NLU data and models. In order to find similar utterances and construct a graph, we use a paraphrase detection model. Results show that applying the inductive graph-based semi-supervised learning can improve the error rate of the NLU model by 5{\%}.
Graph Enhanced Cross-Domain Text-to-SQL Generation
Authors: Siyu Huo, Tengfei Ma, Jie Chen, Maria Chang, Lingfei Wu, Michael Witbrock
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Natural Language Understanding, Semantic Parsing, SQL Parsing, Text to SQL, Text-To-SQL, Translation

Semantic parsing is a fundamental problem in natural language understanding, as it involves the mapping of natural language to structured forms such as executable queries or logic-like knowledge representations. Existing deep learning approaches for semantic parsing have shown promise on a variety of benchmark data sets, particularly on text-to-SQL parsing. However, most text-to-SQL parsers do not generalize to unseen data sets in different domains. In this paper, we propose a new cross-domain learning scheme to perform text-to-SQL translation and demonstrate its use on Spider, a large-scale cross-domain text-to-SQL data set. We improve upon a state-of-the-art Spider model, SyntaxSQLNet, by constructing a graph of column names for all databases and using graph neural networks to compute their embeddings. The resulting embeddings offer better cross-domain representations and SQL queries, as evidenced by substantial improvement on the Spider data set compared to SyntaxSQLNet.
Node Embeddings for Graph Merging: Case of Knowledge Graph Construction
Authors: Ida Szubert, Mark Steedman
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: graph construction, Knowledge Graphs

Combining two graphs requires merging the nodes which are counterparts of each other. In this process errors occur, resulting in incorrect merging or incorrect failure to merge. We find a high prevalence of such errors when using AskNET, an algorithm for building Knowledge Graphs from text corpora. AskNET node matching method uses string similarity, which we propose to replace with vector embedding similarity. We explore graph-based and word-based embedding models and show an overall error reduction of from 56{\%} to 23.6{\%}, with a reduction of over a half in both types of incorrect node matching.
DBee: A Database for Creating and Managing Knowledge Graphs and Embeddings
Authors: Viktor Schlegel, Andr{\'e} Freitas
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Knowledge Graphs

This paper describes DBee, a database to support the construction of data-intensive AI applications. DBee provides a unique data model which operates jointly over large-scale knowledge graphs (KGs) and embedding vector spaces (VSs). This model supports queries which exploit the semantic properties of both types of representations (KGs and VSs). Additionally, DBee aims to facilitate the construction of KGs and VSs, by providing a library of generators, which can be used to create, integrate and transform data into KGs and VSs.
A Constituency Parsing Tree based Method for Relation Extraction from Abstracts of Scholarly Publications
Authors: Ming Jiang, Jana Diesner
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Constituency Parsing, Relation, Relation Extraction

We present a simple, rule-based method for extracting entity networks from the abstracts of scientific literature. By taking advantage of selected syntactic features of constituent parsing trees, our method automatically extracts and constructs graphs in which nodes represent text-based entities (in this case, noun phrases) and their relationships (in this case, verb phrases or preposition phrases). We use two benchmark datasets for evaluation and compare with previously presented results for these data. Our evaluation results show that the proposed method leads to accuracy rates that are comparable to or exceed the results achieved with state-of-the-art, learning-based methods in several cases.
Answering Naturally: Factoid to Full length Answer Generation
Authors: Vaishali Pal, Manish Shrivastava, Irshad Bhat
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Answer Generation, Question Answering, Reading Comprehension, Text Generation

In recent years, the task of Question Answering over passages, also pitched as a reading comprehension, has evolved into a very active research area. A reading comprehension system extracts a span of text, comprising of named entities, dates, small phrases, etc., which serve as the answer to a given question. However, these spans of text would result in an unnatural reading experience in a conversational system. Usually, dialogue systems solve this issue by using template-based language generation. These systems, though adequate for a domain specific task, are too restrictive and predefined for a domain independent system. In order to present the user with a more conversational experience, we propose a pointer generator based full-length answer generator which can be used with most QA systems. Our system generates a full length answer given a question and the extracted factoid/span answer without relying on the passage from where the answer was extracted. We also present a dataset of 315000 question, factoid answer and full length answer triples. We have evaluated our system using ROUGE-1,2,L and BLEU and achieved 74.05 BLEU score and 86.25 Rogue-L score.
Abstractive Timeline Summarization
Authors: Julius Steen, Katja Markert
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Timeline Summarization

Timeline summarization (TLS) automatically identifies key dates of major events and provides short descriptions of what happened on these dates. Previous approaches to TLS have focused on extractive methods. In contrast, we suggest an abstractive timeline summarization system. Our system is entirely unsupervised, which makes it especially suited to TLS where there are very few gold summaries available for training of supervised systems. In addition, we present the first abstractive oracle experiments for TLS. Our system outperforms extractive competitors in terms of ROUGE when the number of input documents is high and the output requires strong compression. In these cases, our oracle experiments confirm that our approach also has a higher upper bound for ROUGE scores than extractive methods. A study with human judges shows that our abstractive system also produces output that is easy to read and understand.
Unsupervised Aspect-Based Multi-Document Abstractive Summarization
Authors: Maximin Coavoux, Hady Elsahar, Matthias Gall{\'e}
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Abstractive Text Summarization, Clustering, Document Summarization, Language Modeling, Language Modelling, Multi-Document Summarization, Opinion Summarization

User-generated reviews of products or services provide valuable information to customers. However, it is often impossible to read each of the potentially thousands of reviews: it would therefore save valuable time to provide short summaries of their contents. We address opinion summarization, a multi-document summarization task, with an unsupervised abstractive summarization neural system. Our system is based on (i) a language model that is meant to encode reviews to a vector space, and to generate fluent sentences from the same vector space (ii) a clustering step that groups together reviews about the same aspects and allows the system to generate summary sentences focused on these aspects. Our experiments on the Oposum dataset empirically show the importance of the clustering step.
Towards Annotating and Creating Summary Highlights at Sub-sentence Level
Authors: Kristjan Arumae, Parminder Bhatia, Fei Liu
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Sentence, Sentence Compression

Highlighting is a powerful tool to pick out important content and emphasize. Creating summary highlights at the sub-sentence level is particularly desirable, because sub-sentences are more concise than whole sentences. They are also better suited than individual words and phrases that can potentially lead to disfluent, fragmented summaries. In this paper we seek to generate summary highlights by annotating summary-worthy sub-sentences and teaching classifiers to do the same. We frame the task as jointly selecting important sentences and identifying a single most informative textual unit from each sentence. This formulation dramatically reduces the task complexity involved in sentence compression. Our study provides new benchmarks and baselines for generating highlights at the sub-sentence level.
Summarizing Relationships for Interactive Concept Map Browsers
Authors: H, Abram ler, Premkumar Ganeshkumar, Brendan O{'}Connor, Mohamed Altantawy
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11

Concept maps are visual summaries, structured as directed graphs: important concepts from a dataset are displayed as vertexes, and edges between vertexes show natural language descriptions of the relationships between the concepts on the map. Thus far, preliminary attempts at automatically creating concept maps have focused on building static summaries. However, in interactive settings, users will need to dynamically investigate particular relationships between pairs of concepts. For instance, a historian using a concept map browser might decide to investigate the relationship between two politicians in a news archive. We present a model which responds to such queries by returning one or more short, importance-ranked, natural language descriptions of the relationship between two requested concepts, for display in a visual interface. Our model is trained on a new public dataset, collected for this task.