pwc-archive/papers-with-abstracts huggingface dataset:
Informative Visual Storytelling with Cross-modal Rules
Authors: Jiacheng Li, Haizhou Shi, Siliang Tang, Fei Wu, Yueting Zhuang
Date: 2019-07-07 00:00:00
Tasks: Decoder, Story Generation, Visual Storytelling

Existing methods in the Visual Storytelling field often suffer from the problem of generating general descriptions, while the image contains a lot of meaningful contents remaining unnoticed. The failure of informative story generation can be concluded to the model's incompetence of capturing enough meaningful concepts. The categories of these concepts include entities, attributes, actions, and events, which are in some cases crucial to grounded storytelling. To solve this problem, we propose a method to mine the cross-modal rules to help the model infer these informative concepts given certain visual input. We first build the multimodal transactions by concatenating the CNN activations and the word indices. Then we use the association rule mining algorithm to mine the cross-modal rules, which will be used for the concept inference. With the help of the cross-modal rules, the generated stories are more grounded and informative. Besides, our proposed method holds the advantages of interpretation, expandability, and transferability, indicating potential for wider application. Finally, we leverage these concepts in our encoder-decoder framework with the attention mechanism. We conduct several experiments on the VIsual StoryTelling~(VIST) dataset, the results of which demonstrate the effectiveness of our approach in terms of both automatic metrics and human evaluation. Additional experiments are also conducted showing that our mined cross-modal rules as additional knowledge helps the model gain better performance when trained on a small dataset.
NIESR: Nuisance Invariant End-to-end Speech Recognition
Authors: I-Hung Hsu, Ayush Jaiswal, Premkumar Natarajan
Date: 2019-07-07 00:00:00
Tasks: speech-recognition, Speech Recognition

Deep neural network models for speech recognition have achieved great success recently, but they can learn incorrect associations between the target and nuisance factors of speech (e.g., speaker identities, background noise, etc.), which can lead to overfitting. While several methods have been proposed to tackle this problem, existing methods incorporate additional information about nuisance factors during training to develop invariant models. However, enumeration of all possible nuisance factors in speech data and the collection of their annotations is difficult and expensive. We present a robust training scheme for end-to-end speech recognition that adopts an unsupervised adversarial invariance induction framework to separate out essential factors for speech-recognition from nuisances without using any supplementary labels besides the transcriptions. Experiments show that the speech recognition model trained with the proposed training scheme achieves relative improvements of 5.48% on WSJ0, 6.16% on CHiME3, and 6.61% on TIMIT dataset over the base model. Additionally, the proposed method achieves a relative improvement of 14.44% on the combined WSJ0+CHiME3 dataset.
Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control
Authors: Amir Pouran Ben Veyseh, Thien Huu Nguyen, Dejing Dou
Date: 2019-07-07 00:00:00
Tasks: Domain Generalization, Relation, Relation Extraction

Relation Extraction (RE) is one of the fundamental tasks in Information Extraction and Natural Language Processing. Dependency trees have been shown to be a very useful source of information for this task. The current deep learning models for relation extraction has mainly exploited this dependency information by guiding their computation along the structures of the dependency trees. One potential problem with this approach is it might prevent the models from capturing important context information beyond syntactic structures and cause the poor cross-domain generalization. This paper introduces a novel method to use dependency trees in RE for deep learning models that jointly predicts dependency and semantics relations. We also propose a new mechanism to control the information flow in the model based on the input entity mentions. Our extensive experiments on benchmark datasets show that the proposed model outperforms the existing methods for RE significantly.
Zero-Shot Open Entity Typing as Type-Compatible Grounding
Authors: Ben Zhou, Daniel Khashabi, Chen-Tse Tsai, Dan Roth
Date: 2019-07-07 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Entity Typing, NER, Vocal Bursts Type Prediction

The problem of entity-typing has been studied predominantly in supervised learning fashion, mostly with task-specific annotations (for coarse types) and sometimes with distant supervision (for fine types). While such approaches have strong performance within datasets, they often lack the flexibility to transfer across text genres and to generalize to new type taxonomies. In this work we propose a zero-shot entity typing approach that requires no annotated data and can flexibly identify newly defined types. Given a type taxonomy defined as Boolean functions of FREEBASE "types", we ground a given mention to a set of type-compatible Wikipedia entries and then infer the target mention's types using an inference algorithm that makes use of the types of these entries. We evaluate our system on a broad range of datasets, including standard fine-grained and coarse-grained entity typing datasets, and also a dataset in the biological domain. Our system is shown to be competitive with state-of-the-art supervised NER systems and outperforms them on out-of-domain datasets. We also show that our system significantly outperforms other zero-shot fine typing systems.
Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures
Authors: Amir Pouran Ben Veyseh, Thien Huu Nguyen, Dejing Dou
Date: 2019-07-07 00:00:00
Proceeding: ACL 2019 7
Tasks: Sentence

Event factuality prediction (EFP) is the task of assessing the degree to which an event mentioned in a sentence has happened. For this task, both syntactic and semantic information are crucial to identify the important context words. The previous work for EFP has only combined these information in a simple way that cannot fully exploit their coordination. In this work, we introduce a novel graph-based neural network for EFP that can integrate the semantic and syntactic information more effectively. Our experiments demonstrate the advantage of the proposed model for EFP.
Joint Lifelong Topic Model and Manifold Ranking for Document Summarization
Authors: Jianying Lin, Rui Liu, Quanye Jia
Date: 2019-07-07 00:00:00
Tasks: Document Summarization, Multi-Document Summarization

Due to the manifold ranking method has a significant effect on the ranking of unknown data based on known data by using a weighted network, many researchers use the manifold ranking method to solve the document summarization task. However, their models only consider the original features but ignore the semantic features of sentences when they construct the weighted networks for the manifold ranking method. To solve this problem, we proposed two improved models based on the manifold ranking method. One is combining the topic model and manifold ranking method (JTMMR) to solve the document summarization task. This model not only uses the original feature, but also uses the semantic feature to represent the document, which can improve the accuracy of the manifold ranking method. The other one is combining the lifelong topic model and manifold ranking method (JLTMMR). On the basis of the JTMMR, this model adds the constraint of knowledge to improve the quality of the topic. At the same time, we also add the constraint of the relationship between documents to dig out a better document semantic features. The JTMMR model can improve the effect of the manifold ranking method by using the better semantic feature. Experiments show that our models can achieve a better result than other baseline models for multi-document summarization task. At the same time, our models also have a good performance on the single document summarization task. After combining with a few basic surface features, our model significantly outperforms some model based on deep learning in recent years. After that, we also do an exploring work for lifelong machine learning by analyzing the effect of adding feedback. Experiments show that the effect of adding feedback to our model is significant.
Evolutionary Algorithm for Sinhala to English Translation
Authors: J. K. Joseph, W. M. T. Chathurika, A. Nugaliyadde, Y. Mallawarachchi
Date: 2019-07-06 00:00:00
Tasks: Machine Translation, Sentence, Translation

Machine Translation (MT) is an area in natural language processing, which focus on translating from one language to another. Many approaches ranging from statistical methods to deep learning approaches are used in order to achieve MT. However, these methods either require a large number of data or a clear understanding about the language. Sinhala language has less digital text which could be used to train a deep neural network. Furthermore, Sinhala has complex rules therefore, it is harder to create statistical rules in order to apply statistical methods in MT. This research focuses on Sinhala to English translation using an Evolutionary Algorithm (EA). EA is used to identifying the correct meaning of Sinhala text and to translate it to English. The Sinhala text is passed to identify the meaning in order to get the correct meaning of the sentence. With the use of the EA the translation is carried out. The translated text is passed on to grammatically correct the sentence. This has shown to achieve accurate results.
Applying a Pre-trained Language Model to Spanish Twitter Humor Prediction
Authors: Bobak Farzin, Piotr Czapla, Jeremy Howard
Date: 2019-07-06 00:00:00
Tasks: Language Modeling, Language Modelling, regression

Our entry into the HAHA 2019 Challenge placed $3^{rd}$ in the classification task and $2^{nd}$ in the regression task. We describe our system and innovations, as well as comparing our results to a Naive Bayes baseline. A large Twitter based corpus allowed us to train a language model from scratch focused on Spanish and transfer that knowledge to our competition model. To overcome the inherent errors in some labels we reduce our class confidence with label smoothing in the loss function. All the code for our project is included in a GitHub repository for easy reference and to enable replication by others.
Best Practices for Learning Domain-Specific Cross-Lingual Embeddings
Authors: Lena Shakurova, Beata Nyari, Chao Li, Mihai Rotaru
Date: 2019-07-06 00:00:00
Proceeding: WS 2019 8
Tasks: Transfer Learning

Cross-lingual embeddings aim to represent words in multiple languages in a shared vector space by capturing semantic similarities across languages. They are a crucial component for scaling tasks to multiple languages by transferring knowledge from languages with rich resources to low-resource languages. A common approach to learning cross-lingual embeddings is to train monolingual embeddings separately for each language and learn a linear projection from the monolingual spaces into a shared space, where the mapping relies on a small seed dictionary. While there are high-quality generic seed dictionaries and pre-trained cross-lingual embeddings available for many language pairs, there is little research on how they perform on specialised tasks. In this paper, we investigate the best practices for constructing the seed dictionary for a specific domain. We evaluate the embeddings on the sequence labelling task of Curriculum Vitae parsing and show that the size of a bilingual dictionary, the frequency of the dictionary words in the domain corpora and the source of data (task-specific vs generic) influence the performance. We also show that the less training data is available in the low-resource language, the more the construction of the bilingual dictionary matters, and demonstrate that some of the choices are crucial in the zero-shot transfer learning case.
ANETAC: Arabic Named Entity Transliteration and Classification Dataset
Authors: Mohamed Seghir Hadj Ameur, Farid Meziane, Ahmed Guessoum
Date: 2019-07-06 00:00:00
Tasks: Classification, General Classification, Translation, Transliteration, Triplet

In this paper, we make freely accessible ANETAC our English-Arabic named entity transliteration and classification dataset that we built from freely available parallel translation corpora. The dataset contains 79,924 instances, each instance is a triplet (e, a, c), where e is the English named entity, a is its Arabic transliteration and c is its class that can be either a Person, a Location, or an Organization. The ANETAC dataset is mainly aimed for the researchers that are working on Arabic named entity transliteration, but it can also be used for named entity classification purposes.
Adaptive Neuro-Surrogate-Based Optimisation Method for Wave Energy Converters Placement Optimisation
Authors: Mehdi Neshat, Ehsan Abbasnejad, Qinfeng Shi, Bradley Alexander, Markus Wagner
Date: 2019-07-06 00:00:00
Tasks: Evolutionary Algorithms

The installed amount of renewable energy has expanded massively in recent years. Wave energy, with its high capacity factors has great potential to complement established sources of solar and wind energy. This study explores the problem of optimising the layout of advanced, three-tether wave energy converters in a size-constrained farm in a numerically modelled ocean environment. Simulating and computing the complicated hydrodynamic interactions in wave farms can be computationally costly, which limits optimisation methods to have just a few thousand evaluations. For dealing with this expensive optimisation problem, an adaptive neuro-surrogate optimisation (ANSO) method is proposed that consists of a surrogate Recurrent Neural Network (RNN) model trained with a very limited number of observations. This model is coupled with a fast meta-heuristic optimiser for adjusting the model's hyper-parameters. The trained model is applied using a greedy local search with a backtracking optimisation strategy. For evaluating the performance of the proposed approach, some of the more popular and successful Evolutionary Algorithms (EAs) are compared in four real wave scenarios (Sydney, Perth, Adelaide and Tasmania). Experimental results show that the adaptive neuro model is competitive with other optimisation methods in terms of total harnessed power output and faster in terms of total computational costs.
Short Text Conversation Based on Deep Neural Network and Analysis on Evaluation Measures
Authors: Hsiang-En Cherng, Chia-Hui Chang
Date: 2019-07-06 00:00:00
Tasks: Question Answering, Sentence, Short-Text Conversation

With the development of Natural Language Processing, Automatic question-answering system such as Waston, Siri, Alexa, has become one of the most important NLP applications. Nowadays, enterprises try to build automatic custom service chatbots to save human resources and provide a 24-hour customer service. Evaluation of chatbots currently relied greatly on human annotation which cost a plenty of time. Thus, has initiated a new Short Text Conversation subtask called Dialogue Quality (DQ) and Nugget Detection (ND) which aim to automatically evaluate dialogues generated by chatbots. In this paper, we solve the DQ and ND subtasks by deep neural network. We proposed two models for both DQ and ND subtasks which is constructed by hierarchical structure: embedding layer, utterance layer, context layer and memory layer, to hierarchical learn dialogue representation from word level, sentence level, context level to long range context level. Furthermore, we apply gating and attention mechanism at utterance layer and context layer to improve the performance. We also tried BERT to replace embedding layer and utterance layer as sentence representation. The result shows that BERT produced a better utterance representation than multi-stack CNN for both DQ and ND subtasks and outperform other models proposed by other researches. The evaluation measures are proposed by , that is, NMD, RSNOD for DQ and JSD, RNSS for ND, which is not traditional evaluation measures such as accuracy, precision, recall and f1-score. Thus, we have done a series of experiments by using traditional evaluation measures and analyze the performance and error.
Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation
Authors: Aizhan Imankulova, Raj Dabre, Atsushi Fujita, Kenji Imamura
Date: 2019-07-06 00:00:00
Proceeding: WS 2019 8
Tasks: Benchmarking, Domain Adaptation, Low Resource Neural Machine Translation, Low-Resource Neural Machine Translation, Machine Translation, NMT, Transfer Learning, Translation

This paper proposes a novel multilingual multistage fine-tuning approach for low-resource neural machine translation (NMT), taking a challenging Japanese--Russian pair for benchmarking. Although there are many solutions for low-resource scenarios, such as multilingual NMT and back-translation, we have empirically confirmed their limited success when restricted to in-domain data. We therefore propose to exploit out-of-domain data through transfer learning, by using it to first train a multilingual NMT model followed by multistage fine-tuning on in-domain parallel and back-translated pseudo-parallel data. Our approach, which combines domain adaptation, multilingualism, and back-translation, helps improve the translation quality by more than 3.7 BLEU points, over a strong baseline, for this extremely low-resource scenario.
BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer
Authors: Guan-Lin Chao, Ian Lane
Date: 2019-07-05 00:00:00
Tasks: Dialogue State Tracking

An important yet rarely tackled problem in dialogue state tracking (DST) is scalability for dynamic ontology (e.g., movie, restaurant) and unseen slot values. We focus on a specific condition, where the ontology is unknown to the state tracker, but the target slot value (except for none and dontcare), possibly unseen during training, can be found as word segment in the dialogue context. Prior approaches often rely on candidate generation from n-gram enumeration or slot tagger outputs, which can be inefficient or suffer from error propagation. We propose BERT-DST, an end-to-end dialogue state tracker which directly extracts slot values from the dialogue context. We use BERT as dialogue context encoder whose contextualized language representations are suitable for scalable DST to identify slot values from their semantic context. Furthermore, we employ encoder parameter sharing across all slots with two advantages: (1) Number of parameters does not grow linearly with the ontology. (2) Language representation knowledge can be transferred among slots. Empirical evaluation shows BERT-DST with cross-slot parameter sharing outperforms prior work on the benchmark scalable DST datasets Sim-M and Sim-R, and achieves competitive performance on the standard DSTC2 and WOZ 2.0 datasets.
Towards Universal Dialogue Act Tagging for Task-Oriented Dialogues
Authors: Shachi Paul, Rahul Goel, Dilek Hakkani-Tür
Date: 2019-07-05 00:00:00
Tasks: Task-Oriented Dialogue Systems

Machine learning approaches for building task-oriented dialogue systems require large conversational datasets with labels to train on. We are interested in building task-oriented dialogue systems from human-human conversations, which may be available in ample amounts in existing customer care center logs or can be collected from crowd workers. Annotating these datasets can be prohibitively expensive. Recently multiple annotated task-oriented human-machine dialogue datasets have been released, however their annotation schema varies across different collections, even for well-defined categories such as dialogue acts (DAs). We propose a Universal DA schema for task-oriented dialogues and align existing annotated datasets with our schema. Our aim is to train a Universal DA tagger (U-DAT) for task-oriented dialogues and use it for tagging human-human conversations. We investigate multiple datasets, propose manual and automated approaches for aligning the different schema, and present results on a target corpus of human-human dialogues. In unsupervised learning experiments we achieve an F1 score of 54.1% on system turns in human-human dialogues. In a semi-supervised setup, the F1 score increases to 57.7% which would otherwise require at least 1.7K manually annotated turns. For new domains, we show further improvements when unlabeled or labeled target domain data is available.
NeuType: A Simple and Effective Neural Network Approach for Predicting Missing Entity Type Information in Knowledge Bases
Authors: Jon Arne Bø Hovda, Darío Garigliotti, Krisztian Balog
Date: 2019-07-05 00:00:00

Knowledge bases store information about the semantic types of entities, which can be utilized in a range of information access tasks. This information, however, is often incomplete, due to new entities emerging on a daily basis. We address the task of automatically assigning types to entities in a knowledge base from a type taxonomy. Specifically, we present two neural network architectures, which take short entity descriptions and, optionally, information about related entities as input. Using the DBpedia knowledge base for experimental evaluation, we demonstrate that these simple architectures yield significant improvements over the current state of the art.
MigrationMiner: An Automated Detection Tool of Third-Party Java Library Migration at the Method Level
Authors: Hussein Alrubaye, Mohamed Wiem Mkaouer, Ali Ouni
Date: 2019-07-05 00:00:00

In this paper we introduce, MigrationMiner, an automated tool that detects code migrations performed between Java third-party library. Given a list of open source projects, the tool detects potential library migration code changes and collects the specific code fragments in which the developer replaces methods from the retired library with methods from the new library. To support the migration process, MigrationMiner collects the library documentation that is associated with every method involved in the migration. We evaluate our tool on a benchmark of manually validated library migrations. Results show that MigrationMiner achieves an accuracy of 100%. A demo video of MigrationMiner is available at https://youtu.be/sAlR1HNetXc.
Un Modelo Ontológico para el Gobierno Electrónico
Authors: Carlos Roberto Brys, José F. Aldana-Montes, David Luis La Red Martínez
Date: 2019-07-04 00:00:00
Tasks: Decision Making

Decision making often requires information that must be Provided with the rich data format. Addressing these new requirements appropriately makes it necessary for government agencies to orchestrate large amounts of information from different sources and formats, to be efficiently delivered through the devices commonly used by people, such as computers, netbooks, tablets and smartphones. To overcome these problems, a model is proposed for the conceptual representation of the State's organizational units, seen as georeferenced entities of Electronic Government, based on ontologies designed under the principles of Linked Open Data, which allows the automatic extraction of information through the machines, which supports the process of governmental decision making and gives citizens full access to find and process through mobile technologies.
A Formal Axiomatization of Computation
Authors: Rasoul Ramezanian
Date: 2019-07-04 00:00:00

We introduce an axiomatization for the notion of computation. Based on the idea of Brouwer choice sequences, we construct a model, denoted by $E$, which satisfies our axioms and $E \models \mathrm{ P \neq NP}$. In other words, regarding "effective computability" in Brouwer intuitionism viewpoint, we show $\mathrm{ P \neq NP}$.