pwc-archive/papers-with-abstracts huggingface dataset:
Integrating Knowledge-Supported Search into the INCEpTION Annotation Platform
Authors: Beto Boullosa, Richard Eckart de Castilho, Naveen Kumar, Jan-Christoph Klie, Iryna Gurevych
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Entity Linking, text annotation

Annotating entity mentions and linking them to a knowledge resource are essential tasks in many domains. It disambiguates mentions, introduces cross-document coreferences, and the resources contribute extra information, e.g. taxonomic relations. Such tasks benefit from text annotation tools that integrate a search which covers the text, the annotations, as well as the knowledge resource. However, to the best of our knowledge, no current tools integrate knowledge-supported search as well as entity linking support. We address this gap by introducing knowledge-supported search functionality into the INCEpTION text annotation platform. In our approach, cross-document references are created by linking entity mentions to a knowledge base in the form of a structured hierarchical vocabulary. The resulting annotations are then indexed to enable fast and yet complex queries taking into account the text, the annotations, and the vocabulary structure.
OpenKE: An Open Toolkit for Knowledge Embedding
Authors: Xu Han, Shulin Cao, Xin Lv, Yankai Lin, Zhiyuan Liu, Maosong Sun, Juanzi Li
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Information Retrieval, Knowledge Graphs, Question Answering, Representation Learning, Retrieval

We release an open toolkit for knowledge embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space. OpenKE prioritizes operational efficiency to support quick model validation and large-scale knowledge representation learning. Meanwhile, OpenKE maintains sufficient modularity and extensibility to easily incorporate new models into the framework. Besides the toolkit, the embeddings of some existing large-scale knowledge graphs pre-trained by OpenKE are also available, which can be directly applied for many applications including information retrieval, personalized recommendation and question answering. The toolkit, documentation, and pre-trained embeddings are all released on \url{http://openke.thunlp.org/}.
LIA: A Natural Language Programmable Personal Assistant
Authors: Igor Labutov, Shashank Srivastava, Tom Mitchell
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Semantic Parsing

We present LIA, an intelligent personal assistant that can be programmed using natural language. Our system demonstrates multiple competencies towards learning from human-like interactions. These include the ability to be taught reusable conditional procedures, the ability to be taught new knowledge about the world (concepts in an ontology) and the ability to be taught how to ground that knowledge in a set of sensors and effectors. Building such a system highlights design questions regarding the overall architecture that such an agent should have, as well as questions about parsing and grounding language in situational contexts. We outline key properties of this architecture, and demonstrate a prototype that embodies them in the form of a personal assistant on an Android device.
PizzaPal: Conversational Pizza Ordering using a High-Density Conversational AI Platform
Authors: Antoine Raux, Yi Ma, Paul Yang, Felicia Wong
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: dialog state tracking, Speech Recognition, Vocal Bursts Intensity Prediction

This paper describes PizzaPal, a voice-only agent for ordering pizza, as well as the Conversational AI architecture built at b4.ai. Based on the principles of high-density conversational AI, it supports natural and flexible interactions through neural conversational language understanding, robust dialog state tracking, and hierarchical task decomposition.
Developing Production-Level Conversational Interfaces with Shallow Semantic Parsing
Authors: Arushi Raghuvanshi, Lucien Carroll, Karthik Raghunathan
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Dialogue Management, General Classification, Intent Classification, Management, Named Entity Recognition (NER), Relation Extraction, Semantic Parsing

We demonstrate an end-to-end approach for building conversational interfaces from prototype to production that has proven to work well for a number of applications across diverse verticals. Our architecture improves on the standard domain-intent-entity classification hierarchy and dialogue management architecture by leveraging shallow semantic parsing. We observe that NLU systems for industry applications often require more structured representations of entity relations than provided by the standard hierarchy, yet without requiring full semantic parses which are often inaccurate on real-world conversational data. We distinguish two kinds of semantic properties that can be provided through shallow semantic parsing: entity groups and entity roles. We also provide live demos of conversational apps built for two different use cases: food ordering and meeting control.
When science journalism meets artificial intelligence : An interactive demonstration
Authors: Raghuram Vadapalli, Bakhtiyar Syed, Nishant Prabhu, Balaji Vasan Srinivasan, Vasudeva Varma
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Articles

We present an online interactive tool that generates titles of blog titles and thus take the first step toward automating science journalism. Science journalism aims to transform jargon-laden scientific articles into a form that the common reader can comprehend while ensuring that the underlying meaning of the article is retained. In this work, we present a tool, which, given the title and abstract of a research paper will generate a blog title by mimicking a human science journalist. The tool makes use of a model trained on a corpus of 87,328 pairs of research papers and their corresponding blogs, built from two science news aggregators. The architecture of the model is a two-stage mechanism which generates blog titles. Evaluation using standard metrics indicate the viability of the proposed system.
Universal Sentence Encoder for English
Authors: Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Brian Strope, Ray Kurzweil
Date: 2018-11-01 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Multi-Task Learning, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings, Transfer Learning

We present easy-to-use TensorFlow Hub sentence embedding models having good task transfer performance. Model variants allow for trade-offs between accuracy and compute resources. We report the relationship between model complexity, resources, and transfer performance. Comparisons are made with baselines without transfer learning and to baselines that incorporate word-level transfer. Transfer learning using sentence-level embeddings is shown to outperform models without transfer learning and often those that use only word-level transfer. We show good transfer task performance with minimal training data and obtain encouraging results on word embedding association tests (WEAT) of model bias.
Continuous Word Embedding Fusion via Spectral Decomposition
Authors: Tianfan Fu, Cheng Zhang, M, Stephan t
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Machine Translation, Transfer Learning, Word Embeddings

Word embeddings have become a mainstream tool in statistical natural language processing. Practitioners often use pre-trained word vectors, which were trained on large generic text corpora, and which are readily available on the web. However, pre-trained word vectors oftentimes lack important words from specific domains. It is therefore often desirable to extend the vocabulary and embed new words into a set of pre-trained word vectors. In this paper, we present an efficient method for including new words from a specialized corpus, containing new words, into pre-trained generic word embeddings. We build on the established view of word embeddings as matrix factorizations to present a spectral algorithm for this task. Experiments on several domain-specific corpora with specialized vocabularies demonstrate that our method is able to embed the new words efficiently into the original embedding space. Compared to competing methods, our method is faster, parameter-free, and deterministic.
A Unified Neural Network Model for Geolocating Twitter Users
Authors: Mohammad Ebrahimi, Elaheh ShafieiBavani, Raymond Wong, Fang Chen
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Disaster Response, News Recommendation

Locations of social media users are important to many applications such as rapid disaster response, targeted advertisement, and news recommendation. However, many users do not share their exact geographical coordinates due to reasons such as privacy concerns. The lack of explicit location information has motivated a growing body of research in recent years looking at different automatic ways of determining the user{'}s primary location. In this paper, we propose a unified user geolocation method which relies on a fusion of neural networks. Our joint model incorporates different types of available information including tweet text, user network, and metadata to predict users{'} locations. Moreover, we utilize a bidirectional LSTM network augmented with an attention mechanism to identify the most location indicative words in textual content of tweets. The experiments demonstrate that our approach achieves state-of-the-art performance over two Twitter benchmark geolocation datasets. We also conduct an ablation study to evaluate the contribution of each type of information in user geolocation performance.
Corpus-Driven Thematic Hierarchy Induction
Authors: Ilia Kuznetsov, Iryna Gurevych
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Machine Translation, Question Answering, Semantic Role Labeling

Thematic role hierarchy is a widely used linguistic tool to describe interactions between semantic roles and their syntactic realizations. Despite decades of dedicated research and numerous thematic hierarchy suggestions in the literature, this concept has not been used in NLP so far due to incompatibility and limited scope of existing hierarchies. We introduce an empirical framework for thematic hierarchy induction and evaluate several role ranking strategies on English and German full-text corpus data. We hypothesize that global thematic hierarchy induction is feasible, that a hierarchy can be induced from just fractions of training data and that resulting hierarchies apply cross-lingually. We evaluate these assumptions empirically.
From Strings to Other Things: Linking the Neighborhood and Transposition Effects in Word Reading
Authors: St{\'e}phan Tulkens, S, Dominiek ra, Walter Daelemans
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10

We investigate the relation between the transposition and deletion effects in word reading, i.e., the finding that readers can successfully read {``}SLAT{''} as {``}SALT{''}, or {``}WRK{''} as {``}WORK{''}, and the neighborhood effect. In particular, we investigate whether lexical orthographic neighborhoods take into account transposition and deletion in determining neighbors. If this is the case, it is more likely that the neighborhood effect takes place early during processing, and does not solely rely on similarity of internal representations. We introduce a new neighborhood measure, rd20, which can be used to quantify neighborhood effects over arbitrary feature spaces. We calculate the rd20 over large sets of words in three languages using various feature sets and show that feature sets that do not allow for transposition or deletion explain more variance in Reaction Time (RT) measurements. We also show that the rd20 can be calculated using the hidden state representations of an Multi-Layer Perceptron, and show that these explain less variance than the raw features. We conclude that the neighborhood effect is unlikely to have a perceptual basis, but is more likely to be the result of items co-activating after recognition. All code is available at: \url{www.github.com/clips/conll2018}
Uncovering Code-Mixed Challenges: A Framework for Linguistically Driven Question Generation and Neural Based Question Answering
Authors: Deepak Gupta, Pabitra Lenka, Asif Ekbal, Pushpak Bhattacharyya
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Question Answering, Question Generation, Question-Generation

Existing research on question answering (QA) and comprehension reading (RC) are mainly focused on the resource-rich language like English. In recent times, the rapid growth of multi-lingual web content has posed several challenges to the existing QA systems. Code-mixing is one such challenge that makes the task more complex. In this paper, we propose a linguistically motivated technique for code-mixed question generation (CMQG) and a neural network based architecture for code-mixed question answering (CMQA). For evaluation, we manually create the code-mixed questions for Hindi-English language pair. In order to show the effectiveness of our neural network based CMQA technique, we utilize two benchmark datasets, SQuAD and MMQA. Experiments show that our proposed model achieves encouraging performance on CMQG and CMQA.
Learning to Embed Semantic Correspondence for Natural Language Understanding
Authors: Sangkeun Jung, Jinsik Lee, Jiwon Kim
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Natural Language Understanding, Re-Ranking, Semantic correspondence, Slot Filling

While learning embedding models has yielded fruitful results in several NLP subfields, most notably Word2Vec, embedding correspondence has relatively not been well explored especially in the context of natural language understanding (NLU), a task that typically extracts structured semantic knowledge from a text. A NLU embedding model can facilitate analyzing and understanding relationships between unstructured texts and their corresponding structured semantic knowledge, essential for both researchers and practitioners of NLU. Toward this end, we propose a framework that learns to embed semantic correspondence between text and its extracted semantic knowledge, called semantic frame. One key contributed technique is semantic frame reconstruction used to derive a one-to-one mapping between embedded vectors and their corresponding semantic frames. Embedding into semantically meaningful vectors and computing their distances in vector space provides a simple, but effective way to measure semantic similarities. With the proposed framework, we demonstrate three key areas where the embedding model can be effective: visualization, semantic search and re-ranking.
Commonsense Knowledge Base Completion and Generation
Authors: Itsumi Saito, Kyosuke Nishida, Hisako Asano, Junji Tomita
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Knowledge Base Completion, Question Answering, Reading Comprehension, Triplet

This study focuses on acquisition of commonsense knowledge. A previous study proposed a commonsense knowledge base completion (CKB completion) method that predicts a confidence score of for triplet-style knowledge for improving the coverage of CKBs. To improve the accuracy of CKB completion and expand the size of CKBs, we formulate a new commonsense knowledge base generation task (CKB generation) and propose a joint learning method that incorporates both CKB completion and CKB generation. Experimental results show that the joint learning method improved completion accuracy and the generation model created reasonable knowledge. Our generation model could also be used to augment data and improve the accuracy of completion.
Learning Text Representations for 500K Classification Tasks on Named Entity Disambiguation
Authors: Ander Barrena, Aitor Soroa, Eneko Agirre
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: All, Data Augmentation, Entity Disambiguation, Entity Linking, Entity Resolution, General Classification, Transfer Learning, Word Embeddings

Named Entity Disambiguation algorithms typically learn a single model for all target entities. In this paper we present a word expert model and train separate deep learning models for each target entity string, yielding 500K classification tasks. This gives us the opportunity to benchmark popular text representation alternatives on this massive dataset. In order to face scarce training data we propose a simple data-augmentation technique and transfer-learning. We show that bag-of-word-embeddings are better than LSTMs for tasks with scarce training data, while the situation is reversed when having larger amounts. Transferring a LSTM which is learned on all datasets is the most effective context representation option for the word experts in all frequency bands. The experiments show that our system trained on out-of-domain Wikipedia data surpass comparable NED systems which have been trained on in-domain training data.
Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment Analysis
Authors: Lishuang Li, Yang Liu, AnQiao Zhou
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Aspect-Based Sentiment Analysis (ABSA), Feature Engineering, Position, Sentence, Sentiment Analysis

Aspect-level sentiment analysis aims to identify the sentiment of a specific target in its context. Previous works have proved that the interactions between aspects and the contexts are important. On this basis, we also propose a succinct hierarchical attention based mechanism to fuse the information of targets and the contextual words. In addition, most existing methods ignore the position information of the aspect when encoding the sentence. In this paper, we argue that the position-aware representations are beneficial to this task. Therefore, we propose a hierarchical attention based position-aware network (HAPN), which introduces position embeddings to learn the position-aware representations of sentences and further generate the target-specific representations of contextual words. The experimental results on SemEval 2014 dataset show that our approach outperforms the state-of-the-art methods.
Bidirectional Generative Adversarial Networks for Neural Machine Translation
Authors: Zhirui Zhang, Shujie Liu, Mu Li, Ming Zhou, Enhong Chen
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Generative Adversarial Network, Language Modeling, Language Modelling, Machine Translation, NMT, Sentence, Translation

Generative Adversarial Network (GAN) has been proposed to tackle the exposure bias problem of Neural Machine Translation (NMT). However, the discriminator typically results in the instability of the GAN training due to the inadequate training problem: the search space is so huge that sampled translations are not sufficient for discriminator training. To address this issue and stabilize the GAN training, in this paper, we propose a novel Bidirectional Generative Adversarial Network for Neural Machine Translation (BGAN-NMT), which aims to introduce a generator model to act as the discriminator, whereby the discriminator naturally considers the entire translation space so that the inadequate training problem can be alleviated. To satisfy this property, generator and discriminator are both designed to model the joint probability of sentence pairs, with the difference that, the generator decomposes the joint probability with a source language model and a source-to-target translation model, while the discriminator is formulated as a target language model and a target-to-source translation model. To further leverage the symmetry of them, an auxiliary GAN is introduced and adopts generator and discriminator models of original one as its own discriminator and generator respectively. Two GANs are alternately trained to update the parameters. Experiment results on German-English and Chinese-English translation tasks demonstrate that our method not only stabilizes GAN training but also achieves significant improvements over baseline systems.
Latent Entities Extraction: How to Extract Entities that Do Not Appear in the Text?
Authors: Eylon Shoshan, Kira Radinsky
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Multi-Task Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Question Answering, Sentence

Named-entity Recognition (NER) is an important task in the NLP field , and is widely used to solve many challenges. However, in many scenarios, not all of the entities are explicitly mentioned in the text. Sometimes they could be inferred from the context or from other indicative words. Consider the following sentence: {``}CMA can easily hydrolyze into free acetic acid.{''} Although water is not mentioned explicitly, one can infer that H2O is an entity involved in the process. In this work, we present the problem of Latent Entities Extraction (LEE). We present several methods for determining whether entities are discussed in a text, even though, potentially, they are not explicitly written. Specifically, we design a neural model that handles extraction of multiple entities jointly. We show that our model, along with multi-task learning approach and a novel task grouping algorithm, reaches high performance in identifying latent entities. Our experiments are conducted on a large biological dataset from the biochemical field. The dataset contains text descriptions of biological processes, and for each process, all of the involved entities in the process are labeled, including implicitly mentioned ones. We believe LEE is a task that will significantly improve many NER and subsequent applications and improve text understanding and inference.
Multi-Modal Sequence Fusion via Recursive Attention for Emotion Recognition
Authors: Rory Beard, Ritwik Das, Raymond W. M. Ng, P. G. Keerthana Gopalakrishnan, Luka Eerens, Pawel Swietojanski, Ondrej Miksik
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Emotion Recognition

Natural human communication is nuanced and inherently multi-modal. Humans possess specialised sensoria for processing vocal, visual, and linguistic, and para-linguistic information, but form an intricately fused percept of the multi-modal data stream to provide a holistic representation. Analysis of emotional content in face-to-face communication is a cognitive task to which humans are particularly attuned, given its sociological importance, and poses a difficult challenge for machine emulation due to the subtlety and expressive variability of cross-modal cues. Inspired by the empirical success of recent so-called End-To-End Memory Networks and related works, we propose an approach based on recursive multi-attention with a shared external memory updated over multiple gated iterations of analysis. We evaluate our model across several large multi-modal datasets and show that global contextualised memory with gated memory update can effectively achieve emotion recognition.
Similarity Dependent Chinese Restaurant Process for Cognate Identification in Multilingual Wordlists
Authors: Taraka Rama
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Clustering, Language Identification

We present and evaluate two similarity dependent Chinese Restaurant Process (sd-CRP) algorithms at the task of automated cognate detection. The sd-CRP clustering algorithms do not require any predefined threshold for detecting cognate sets in a multilingual word list. We evaluate the performance of the algorithms on six language families (more than 750 languages) and find that both the sd-CRP variants performs as well as InfoMap and better than UPGMA at the task of inferring cognate clusters. The algorithms presented in this paper are family agnostic and can be applied to any linguistically under-studied language family.