pwc-archive/papers-with-abstracts huggingface dataset:
Apples to Apples: Learning Semantics of Common Entities Through a Novel Comprehension Task
Authors: Bakhsh, Omid eh, James Allen
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Benchmarking, Part-Of-Speech Tagging, Reading Comprehension, Semantic Textual Similarity, Word Embeddings

Understanding common entities and their attributes is a primary requirement for any system that comprehends natural language. In order to enable learning about common entities, we introduce a novel machine comprehension task, GuessTwo: given a short paragraph comparing different aspects of two real-world semantically-similar entities, a system should guess what those entities are. Accomplishing this task requires deep language understanding which enables inference, connecting each comparison paragraph to different levels of knowledge about world entities and their attributes. So far we have crowdsourced a dataset of more than 14K comparison paragraphs comparing entities from a variety of categories such as fruits and animals. We have designed two schemes for evaluation: open-ended, and binary-choice prediction. For benchmarking further progress in the task, we have collected a set of paragraphs as the test set on which human can accomplish the task with an accuracy of 94.2{\%} on open-ended prediction. We have implemented various models for tackling the task, ranging from semantic-driven to neural models. The semantic-driven approach outperforms the neural models, however, the results indicate that the task is very challenging across the models.
Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees
Authors: Arzoo Katiyar, Claire Cardie
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Relation Extraction

We present a novel attention-based recurrent neural network for joint extraction of entity mentions and relations. We show that attention along with long short term memory (LSTM) network can extract semantic relations between entity mentions without having access to dependency trees. Experiments on Automatic Content Extraction (ACE) corpora show that our model significantly outperforms feature-based joint model by Li and Ji (2014). We also compare our model with an end-to-end tree-based LSTM model (SPTree) by Miwa and Bansal (2016) and show that our model performs within 1{\%} on entity mentions and 2{\%} on relations. Our fine-grained analysis also shows that our model performs significantly better on Agent-Artifact relations, while SPTree performs better on Physical and Part-Whole relations.
Semantic Word Clusters Using Signed Spectral Clustering
Authors: Jo{\~a}o Sedoc, Jean Gallier, Dean Foster, Lyle Ungar
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Clustering, Graph Clustering, Semantic Textual Similarity, Word Embeddings, Word Similarity

Vector space representations of words capture many aspects of word similarity, but such methods tend to produce vector spaces in which antonyms (as well as synonyms) are close to each other. For spectral clustering using such word embeddings, words are points in a vector space where synonyms are linked with positive weights, while antonyms are linked with negative weights. We present a new signed spectral normalized graph cut algorithm, \textit{signed clustering}, that overlays existing thesauri upon distributionally derived vector representations of words, so that antonym relationships between word pairs are represented by negative weights. Our signed clustering algorithm produces clusters of words that simultaneously capture distributional and synonym relations. By using randomized spectral decomposition (Halko et al., 2011) and sparse matrices, our method is both fast and scalable. We validate our clusters using datasets containing human judgments of word pair similarities and show the benefit of using our word clusters for sentiment prediction.
Don't understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures
Authors: Iryna Haponchyk, Aless Moschitti, ro
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: coreference-resolution, Coreference Resolution, Structured Prediction

An interesting aspect of structured prediction is the evaluation of an output structure against the gold standard. Especially in the loss-augmented setting, the need of finding the max-violating constraint has severely limited the expressivity of effective loss functions. In this paper, we trade off exact computation for enabling the use and study of more complex loss functions for coreference resolution. Most interestingly, we show that such functions can be (i) automatically learned also from controversial but commonly accepted coreference measures, e.g., MELA, and (ii) successfully used in learning algorithms. The accurate model comparison on the standard CoNLL-2012 setting shows the benefit of more expressive loss functions.
Bayesian Modeling of Lexical Resources for Low-Resource Settings
Authors: Nicholas Andrews, Mark Dredze, Benjamin Van Durme, Jason Eisner
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Low Resource Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Lexical resources such as dictionaries and gazetteers are often used as auxiliary data for tasks such as part-of-speech induction and named-entity recognition. However, discriminative training with lexical features requires annotated data to reliably estimate the lexical feature weights and may result in overfitting the lexical features at the expense of features which generalize better. In this paper, we investigate a more robust approach: we stipulate that the lexicon is the result of an assumed generative process. Practically, this means that we may treat the lexical resources as observations under the proposed generative model. The lexical resources provide training data for the generative model without requiring separate data to estimate lexical feature weights. We evaluate the proposed approach in two settings: part-of-speech induction and low-resource named-entity recognition.
Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization
Authors: Maxime Peyrard, Judith Eckle-Kohler
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Document Summarization, Multi-Document Summarization, Open Information Extraction, Text Summarization

We present a new supervised framework that learns to estimate automatic Pyramid scores and uses them for optimization-based extractive multi-document summarization. For learning automatic Pyramid scores, we developed a method for automatic training data generation which is based on a genetic algorithm using automatic Pyramid as the fitness function. Our experimental evaluation shows that our new framework significantly outperforms strong baselines regarding automatic Pyramid, and that there is much room for improvement in comparison with the upper-bound for automatic Pyramid.
PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents
Authors: Corina Florescu, Cornelia Caragea
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Information Retrieval, Keyphrase Extraction

The large and growing amounts of online scholarly data present both challenges and opportunities to enhance knowledge discovery. One such challenge is to automatically extract a small set of keyphrases from a document that can accurately describe the document{'}s content and can facilitate fast information processing. In this paper, we propose PositionRank, an unsupervised model for keyphrase extraction from scholarly documents that incorporates information from all positions of a word{'}s occurrences into a biased PageRank. Our model obtains remarkable improvements in performance over PageRank models that do not take into account word positions as well as over strong baselines for this task. Specifically, on several datasets of research papers, PositionRank achieves improvements as high as 29.09{\%}.
Visualizing and Understanding Neural Machine Translation
Authors: Yanzhuo Ding, Yang Liu, Huanbo Luan, Maosong Sun
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Decoder, Machine Translation, NMT, Translation

While neural machine translation (NMT) has made remarkable progress in recent years, it is hard to interpret its internal workings due to the continuous representations and non-linearity of neural networks. In this work, we propose to use layer-wise relevance propagation (LRP) to compute the contribution of each contextual word to arbitrary hidden states in the attention-based encoder-decoder framework. We show that visualization with LRP helps to interpret the internal workings of NMT and analyze translation errors.
Detecting annotation noise in automatically labelled data
Authors: Ines Rehbein, Josef Ruppenhofer
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Active Learning, Domain Adaptation, Language Modeling, Language Modelling, Named Entity Recognition (NER)

We introduce a method for error detection in automatically annotated text, aimed at supporting the creation of high-quality language resources at affordable cost. Our method combines an unsupervised generative model with human supervision from active learning. We test our approach on in-domain and out-of-domain data in two languages, in AL simulations and in a real world setting. For all settings, the results show that our method is able to detect annotation errors with high precision and high recall.
Abstractive Document Summarization with a Graph-Based Attentional Neural Model
Authors: Jiwei Tan, Xiaojun Wan, Jianguo Xiao
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Abstractive Text Summarization, Document Summarization, Image Captioning, Machine Translation, Sentence, Sentence Summarization, Text Generation, Text Summarization

Abstractive summarization is the ultimate goal of document summarization research, but previously it is less investigated due to the immaturity of text generation techniques. Recently impressive progress has been made to abstractive sentence summarization using neural models. Unfortunately, attempts on abstractive document summarization are still in a primitive stage, and the evaluation results are worse than extractive methods on benchmark datasets. In this paper, we review the difficulties of neural abstractive document summarization, and propose a novel graph-based attention mechanism in the sequence-to-sequence framework. The intuition is to address the saliency factor of summarization, which has been overlooked by prior works. Experimental results demonstrate our model is able to achieve considerable improvement over previous neural abstractive models. The data-driven neural abstractive method is also competitive with state-of-the-art extractive methods.
Neural Joint Model for Transition-based Chinese Syntactic Analysis
Authors: Shuhei Kurita, Daisuke Kawahara, Sadao Kurohashi
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Chinese Word Segmentation, Dependency Parsing, POS, POS Tagging, Segmentation, Word Embeddings

We present neural network-based joint models for Chinese word segmentation, POS tagging and dependency parsing. Our models are the first neural approaches for fully joint Chinese analysis that is known to prevent the error propagation problem of pipeline models. Although word embeddings play a key role in dependency parsing, they cannot be applied directly to the joint task in the previous work. To address this problem, we propose embeddings of character strings, in addition to words. Experiments show that our models outperform existing systems in Chinese word segmentation and POS tagging, and perform preferable accuracies in dependency parsing. We also explore bi-LSTM models with fewer features.
A Local Detection Approach for Named Entity Recognition and Mention Detection
Authors: Mingbin Xu, Hui Jiang, Sedtawut Watcharawittayakul
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Feature Engineering, Image Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence, Speech Recognition

In this paper, we study a novel approach for named entity recognition (NER) and mention detection (MD) in natural language processing. Instead of treating NER as a sequence labeling problem, we propose a new local detection approach, which relies on the recent fixed-size ordinally forgetting encoding (FOFE) method to fully encode each sentence fragment and its left/right contexts into a fixed-size representation. Subsequently, a simple feedforward neural network (FFNN) is learned to either reject or predict entity label for each individual text fragment. The proposed method has been evaluated in several popular NER and MD tasks, including CoNLL 2003 NER task and TAC-KBP2015 and TAC-KBP2016 Tri-lingual Entity Discovery and Linking (EDL) tasks. Our method has yielded pretty strong performance in all of these examined tasks. This local detection approach has shown many advantages over the traditional sequence labeling methods.
Vancouver Welcomes You! Minimalist Location Metonymy Resolution
Authors: Milan Gritta, Mohammad Taher Pilehvar, Nut Limsopatham, Nigel Collier
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Named entities are frequently used in a metonymic manner. They serve as references to related entities such as people and organisations. Accurate identification and interpretation of metonymy can be directly beneficial to various NLP applications, such as Named Entity Recognition and Geographical Parsing. Until now, metonymy resolution (MR) methods mainly relied on parsers, taggers, dictionaries, external word lists and other handcrafted lexical resources. We show how a minimalist neural approach combined with a novel predicate window method can achieve competitive results on the SemEval 2007 task on Metonymy Resolution. Additionally, we contribute with a new Wikipedia-based MR dataset called RelocaR, which is tailored towards locations as well as improving previous deficiencies in annotation guidelines.
Unifying Text, Metadata, and User Network Representations with a Neural Network for Geolocation Prediction
Authors: Yasuhide Miura, Motoki Taniguchi, Tomoki Taniguchi, Tomoko Ohkuma
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Prediction, Sentiment Analysis

We propose a novel geolocation prediction model using a complex neural network. Geolocation prediction in social media has attracted many researchers to use information of various types. Our model unifies text, metadata, and user network representations with an attention mechanism to overcome previous ensemble approaches. In an evaluation using two open datasets, the proposed model exhibited a maximum 3.8{\%} increase in accuracy and a maximum of 6.6{\%} increase in accuracy@161 against previous models. We further analyzed several intermediate layers of our model, which revealed that their states capture some statistical characteristics of the datasets.
Enriching Complex Networks with Word Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts
Authors: Le Santos, ro, Edilson Anselmo Corr{\^e}a J{\'u}nior, Osvaldo Oliveira Jr, Diego Amancio, Let{\'\i}cia Mansur, S Alu{\'\i}sio, ra
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Binary Classification, Word Embeddings

Mild Cognitive Impairment (MCI) is a mental disorder difficult to diagnose. Linguistic features, mainly from parsers, have been used to detect MCI, but this is not suitable for large-scale assessments. MCI disfluencies produce non-grammatical speech that requires manual or high precision automatic correction of transcripts. In this paper, we modeled transcripts into complex networks and enriched them with word embedding (CNE) to better represent short texts produced in neuropsychological assessments. The network measurements were applied with well-known classifiers to automatically identify MCI in transcripts, in a binary classification task. A comparison was made with the performance of traditional approaches using Bag of Words (BoW) and linguistic features for three datasets: DementiaBank in English, and Cinderella and Arizona-Battery in Portuguese. Overall, CNE provided higher accuracy than using only complex networks, while Support Vector Machine was superior to other classifiers. CNE provided the highest accuracies for DementiaBank and Cinderella, but BoW was more efficient for the Arizona-Battery dataset probably owing to its short narratives. The approach using linguistic features yielded higher accuracy if the transcriptions of the Cinderella dataset were manually revised. Taken together, the results indicate that complex networks enriched with embedding is promising for detecting MCI in large-scale assessments.
Adversarial Adaptation of Synthetic or Stale Data
Authors: Young-Bum Kim, Karl Stratos, Dongchan Kim
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Domain Adaptation, Spoken Language Understanding

Two types of data shift common in practice are 1. transferring from synthetic data to live user data (a deployment shift), and 2. transferring from stale data to current data (a temporal shift). Both cause a distribution mismatch between training and evaluation, leading to a model that overfits the flawed training data and performs poorly on the test data. We propose a solution to this mismatch problem by framing it as domain adaptation, treating the flawed training dataset as a source domain and the evaluation dataset as a target domain. To this end, we use and build on several recent advances in neural domain adaptation such as adversarial training (Ganinet al., 2016) and domain separation network (Bousmalis et al., 2016), proposing a new effective adversarial training scheme. In both supervised and unsupervised adaptation scenarios, our approach yields clear improvement over strong baselines.
A Neural Local Coherence Model
Authors: Dat Tien Nguyen, Shafiq Joty
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: model, Text Generation

We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.
Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG)
Authors: Tomer Cagan, Stefan L. Frank, Reut Tsarfaty
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Articles, Language Modeling, Language Modelling, Text Generation, Topic Models

Opinionated Natural Language Generation (ONLG) is a new, challenging, task that aims to automatically generate human-like, subjective, responses to opinionated articles online. We present a data-driven architecture for ONLG that generates subjective responses triggered by users{'} agendas, consisting of topics and sentiments, and based on wide-coverage automatically-acquired generative grammars. We compare three types of grammatical representations that we design for ONLG, which interleave different layers of linguistic information and are induced from a new, enriched dataset we developed. Our evaluation shows that generation with Relational-Realizational (Tsarfaty and Sima{'}an, 2008) inspired grammar gets better language model scores than lexicalized grammars {`}a la Collins (2003), and that the latter gets better human-evaluation scores. We also show that conditioning the generation on topic models makes generated responses more relevant to the document content.
Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback
Authors: Avinesh P. V. S, Christian M. Meyer
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Active Learning, Document Summarization, Multi-Document Summarization

In this paper, we propose an extractive multi-document summarization (MDS) system using joint optimization and active learning for content selection grounded in user feedback. Our method interactively obtains user feedback to gradually improve the results of a state-of-the-art integer linear programming (ILP) framework for MDS. Our methods complement fully automatic methods in producing high-quality summaries with a minimum number of iterations and feedbacks. We conduct multiple simulation-based experiments and analyze the effect of feedback-based concept selection in the ILP setup in order to maximize the user-desired content in the summary.
Learning to Generate Market Comments from Stock Prices
Authors: Soichiro Murakami, Akihiko Watanabe, Akira Miyazawa, Keiichi Goshima, Toshihiko Yanase, Hiroya Takamura, Yusuke Miyao
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Decoder, Informativeness, Text Generation, Time Series Analysis

This paper presents a novel encoder-decoder model for automatically generating market comments from stock prices. The model first encodes both short- and long-term series of stock prices so that it can mention short- and long-term changes in stock prices. In the decoding phase, our model can also generate a numerical value by selecting an appropriate arithmetic operation such as subtraction or rounding, and applying it to the input stock prices. Empirical experiments show that our best model generates market comments at the fluency and the informativeness approaching human-generated reference texts.
Can Syntax Help? Improving an LSTM-based Sentence Compression Model for New Domains
Authors: Liangguo Wang, Jing Jiang, Hai Leong Chieu, Chen Hui Ong, D. Song, an, Lejian Liao
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Sentence, Sentence Compression, Text Summarization

In this paper, we study how to improve the domain adaptability of a deletion-based Long Short-Term Memory (LSTM) neural network model for sentence compression. We hypothesize that syntactic information helps in making such models more robust across domains. We propose two major changes to the model: using explicit syntactic features and introducing syntactic constraints through Integer Linear Programming (ILP). Our evaluation shows that the proposed model works better than the original model as well as a traditional non-neural-network-based model in a cross-domain setting.
Transductive Non-linear Learning for Chinese Hypernym Prediction
Authors: Chengyu Wang, Junchi Yan, Aoying Zhou, Xiaofeng He
Date: 2017-07-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Prediction, Relation Extraction, Transductive Learning

Finding the correct hypernyms for entities is essential for taxonomy learning, fine-grained entity categorization, query understanding, etc. Due to the flexibility of the Chinese language, it is challenging to identify hypernyms in Chinese accurately. Rather than extracting hypernyms from texts, in this paper, we present a transductive learning approach to establish mappings from entities to hypernyms in the embedding space directly. It combines linear and non-linear embedding projection models, with the capacity of encoding arbitrary language-specific rules. Experiments on real-world datasets illustrate that our approach outperforms previous methods for Chinese hypernym prediction.