yuz9yuz/NLPImpact huggingface dataset:
Title: Integrating Morphological Desegmentation into Phrase-based Decoding
Venue: NAACL (2016)
Topic: machine learning for nlp
Title: The Instantiation Discourse Relation: A Corpus Analysis of Its Properties and Improved Detection
Venue: NAACL (2016)
Topic: discourse and pragmatics
Title: Sparse Bilingual Word Representations for Cross-lingual Lexical Entailment
Venue: NAACL (2016)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Title: Automatic Prediction of Linguistic Decline in Writings of Subjects with Degenerative Dementia
Venue: NAACL (2016)
Topic: nlp applications
Title: Consensus Maximization Fusion of Probabilistic Information Extractors
Venue: NAACL (2016)
Topic: information extraction
Title: Simple, Fast Noise-Contrastive Estimation for Large RNN Vocabularies
Venue: NAACL (2016)
Topic: low-resource methods for nlp
Title: Automatically Inferring Implicit Properties in Similes
Venue: NAACL (2016)
Topic: resources and evaluation
Title: Visual Storytelling
Venue: NAACL (2016)
Topic: multimodality and language grounding to vision, robotics and beyond
Title: PRIMT: A Pick-Revise Framework for Interactive Machine Translation
Venue: NAACL (2016)
Topic: machine translation
Title: Incorporating Side Information into Recurrent Neural Network Language Models
Venue: NAACL (2016)
Topic: machine learning for nlp
Title: Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks
Venue: NAACL (2016)
Topic: information retrieval and text mining
Title: K-Embeddings: Learning Conceptual Embeddings for Words using Context
Venue: NAACL (2016)
Topic: interpretability and analysis of models for nlp
Title: Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking
Venue: NAACL (2016)
Topic: nlp applications
Title: Semi-supervised Question Retrieval with Gated Convolutions
Venue: NAACL (2016)
Topic: information retrieval and text mining
Title: This is how we do it: Answer Reranking for Open-domain How Questions with Paragraph Vectors and Minimal Feature Engineering
Venue: NAACL (2016)
Topic: nlp applications
Title: Multilingual Language Processing From Bytes
Venue: NAACL (2016)
Topic: multilinguality and language diversity
Title: Ten Pairs to Tag – Multilingual POS Tagging via Coarse Mapping between Embeddings
Venue: NAACL (2016)
Topic: syntax: tagging, chunking and parsing
Title: Part-of-Speech Tagging for Historical English
Venue: NAACL (2016)
Topic: syntax: tagging, chunking and parsing
Title: Statistical Modeling of Creole Genesis
Venue: NAACL (2016)
Topic: nlp applications
Title: Shallow Parsing Pipeline - Hindi-English Code-Mixed Social Media Text
Venue: NAACL (2016)
Topic: syntax: tagging, chunking and parsing
Title: Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders
Venue: NAACL (2016)
Topic: language modeling
Title: Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning
Venue: NAACL (2016)
Topic: phonology, morphology, and word segmentation
Title: Learning Distributed Representations of Sentences from Unlabelled Data
Venue: NAACL (2016)
Topic: interpretability and analysis of models for nlp
Title: Retrofitting Sense-Specific Word Vectors Using Parallel Text
Venue: NAACL (2016)
Topic: language modeling
Title: End-to-End Argumentation Mining in Student Essays
Venue: NAACL (2016)
Topic: nlp applications
Title: Cross-Domain Mining of Argumentative Text through Distant Supervision
Venue: NAACL (2016)
Topic: sentiment analysis, stylistic analysis, and argument mining
Title: A Study of the Impact of Persuasive Argumentation in Political Debates
Venue: NAACL (2016)
Topic: computational social science and cultural analytics
Title: Lexical Coherence Graph Modeling Using Word Embeddings
Venue: NAACL (2016)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Title: Using Context to Predict the Purpose of Argumentative Writing Revisions
Venue: NAACL (2016)
Topic: discourse and pragmatics
Title: Automatic Generation and Scoring of Positive Interpretations from Negated Statements
Venue: NAACL (2016)
Topic: interpretability and analysis of models for nlp
Title: Learning Natural Language Inference with LSTM
Venue: NAACL (2016)
Topic: machine learning for nlp
Title: Activity Modeling in Email
Venue: NAACL (2016)
Topic: nlp applications
Title: Clustering Paraphrases by Word Sense
Venue: NAACL (2016)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Title: Unsupervised Learning of Prototypical Fillers for Implicit Semantic Role Labeling
Venue: NAACL (2016)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Title: Hierarchical Attention Networks for Document Classification
Venue: NAACL (2016)
Topic: machine learning for nlp
Title: Dependency Based Embeddings for Sentence Classification Tasks
Venue: NAACL (2016)
Topic: syntax: tagging, chunking and parsing
Title: Deep LSTM based Feature Mapping for Query Classification
Venue: NAACL (2016)
Topic: low-resource methods for nlp
Title: Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents
Venue: NAACL (2016)
Topic: information extraction
Title: MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification
Venue: NAACL (2016)
Topic: low-resource methods for nlp
Title: Improving sentence compression by learning to predict gaze
Venue: NAACL (2016)
Topic: sentiment analysis, stylistic analysis, and argument mining
Title: Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships
Venue: NAACL (2016)
Topic: computational social science and cultural analytics
Title: Learning to Compose Neural Networks for Question Answering
Venue: NAACL (2016)
Topic: question answering
Title: An End-to-end Approach to Learning Semantic Frames with Feedforward Neural Network
Venue: NAACL (2016)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Title: Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn’t.
Venue: NAACL (2016)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Title: Argument Identification in Chinese Editorials
Venue: NAACL (2016)
Topic: sentiment analysis, stylistic analysis, and argument mining
Title: Automatic tagging and retrieval of E-Commerce products based on visual features
Venue: NAACL (2016)
Topic: multimodality and language grounding to vision, robotics and beyond
Title: Combining syntactic patterns and Wikipedia’s hierarchy of hyperlinks to extract meronym relations
Venue: NAACL (2016)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Title: Data-driven Paraphrasing and Stylistic Harmonization
Venue: NAACL (2016)
Topic: phonology, morphology, and word segmentation
Title: Detecting “Smart” Spammers on Social Network: A Topic Model Approach
Venue: NAACL (2016)
Topic: computational social science and cultural analytics
Title: Developing language technology tools and resources for a resource-poor language: Sindhi
Venue: NAACL (2016)
Topic: multilinguality and language diversity
Title: Effects of Communicative Pressures on Novice L2 Learners’ Use of Optional Formal Devices
Venue: NAACL (2016)
Topic: discourse and pragmatics
Title: Explicit Argument Identification for Discourse Parsing In Hindi: A Hybrid Pipeline
Venue: NAACL (2016)
Topic: discourse and pragmatics
Title: Exploring Fine-Grained Emotion Detection in Tweets
Venue: NAACL (2016)
Topic: computational social science and cultural analytics
Title: Extraction of Bilingual Technical Terms for Chinese-Japanese Patent Translation
Venue: NAACL (2016)
Topic: machine translation
Title: Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter
Venue: NAACL (2016)
Topic: computational social science and cultural analytics
Title: Non-decreasing Sub-modular Function for Comprehensible Summarization
Venue: NAACL (2016)
Topic: summarization
Title: Phylogenetic simulations over constraint-based grammar formalisms
Venue: NAACL (2016)
Topic: phonology, morphology, and word segmentation
Title: Question Answering over Knowledge Base using Factual Memory Networks
Venue: NAACL (2016)
Topic: question answering
Title: Using Related Languages to Enhance Statistical Language Models
Venue: NAACL (2016)
Topic: language modeling
Title: rstWeb - A Browser-based Annotation Interface for Rhetorical Structure Theory and Discourse Relations
Venue: NAACL (2016)
Topic: discourse and pragmatics
Title: Instant Feedback for Increasing the Presence of Solutions in Peer Reviews
Venue: NAACL (2016)
Topic: nlp applications
Title: Farasa: A Fast and Furious Segmenter for Arabic
Venue: NAACL (2016)
Topic: low-resource methods for nlp
Title: iAppraise: A Manual Machine Translation Evaluation Environment Supporting Eye-tracking
Venue: NAACL (2016)
Topic: resources and evaluation
Title: Linguistica 5: Unsupervised Learning of Linguistic Structure
Venue: NAACL (2016)
Topic: linguistic theories, cognitive modeling and psycholinguistics
Title: TransRead: Designing a Bilingual Reading Experience with Machine Translation Technologies
Venue: NAACL (2016)
Topic: machine translation
Title: New Dimensions in Testimony Demonstration
Venue: NAACL (2016)
Topic: resources and evaluation
Title: ArgRewrite: A Web-based Revision Assistant for Argumentative Writings
Venue: NAACL (2016)
Topic: nlp applications
Title: Scaling Up Word Clustering
Venue: NAACL (2016)
Topic: generation
Title: Task Completion Platform: A self-serve multi-domain goal oriented dialogue platform
Venue: NAACL (2016)
Topic: dialogue and interactive systems
Title: Illinois Math Solver: Math Reasoning on the Web
Venue: NAACL (2016)
Topic: nlp applications
Title: LingoTurk: managing crowdsourced tasks for psycholinguistics
Venue: NAACL (2016)
Topic: linguistic theories, cognitive modeling and psycholinguistics
Title: Sentential Paraphrasing as Black-Box Machine Translation
Venue: NAACL (2016)
Topic: machine translation
Title: A Tag-based English Math Word Problem Solver with Understanding, Reasoning and Explanation
Venue: NAACL (2016)
Topic: question answering
Title: Cross-media Event Extraction and Recommendation
Venue: NAACL (2016)
Topic: information extraction
Title: SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization
Venue: NAACL (2016)
Topic: machine learning for nlp
Title: Lecture Translator - Speech translation framework for simultaneous lecture translation
Venue: NAACL (2016)
Topic: machine translation
Title: Zara The Supergirl: An Empathetic Personality Recognition System
Venue: NAACL (2016)
Topic: computational social science and cultural analytics
Title: Kathaa: A Visual Programming Framework for NLP Applications
Venue: NAACL (2016)
Topic: multimodality and language grounding to vision, robotics and beyond
Title: “Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Venue: NAACL (2016)
Topic: interpretability and analysis of models for nlp
Title: English Resource Semantics
Venue: NAACL (2016)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Title: Multilingual Multimodal Language Processing Using Neural Networks
Venue: NAACL (2016)
Topic: multilinguality and language diversity
Title: Question Answering with Knowledge Base, Web and Beyond
Venue: NAACL (2016)
Topic: question answering
Title: Recent Progress in Deep Learning for NLP
Venue: NAACL (2016)
Topic: machine learning for nlp
Title: Scalable Statistical Relational Learning for NLP
Venue: NAACL (2016)
Topic: low-resource methods for nlp
Title: Statistical Machine Translation between Related Languages
Venue: NAACL (2016)
Topic: machine translation
Title: Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition
Venue: NAACL (2018)
Topic: information extraction
Abstract: We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining. Medical records which are written by clinicians from different specialties usually contain quite different terminologies and writing styles. The difference of specialties and the cost of human annotation makes it particularly difficult to train a universal medical NER system. In this paper, we propose a label-aware double transfer learning framework (La-DTL) for cross-specialty NER, so that a medical NER system designed for one specialty could be conveniently applied to another one with minimal annotation efforts. The transferability is guaranteed by two components: (i) we propose label-aware MMD for feature representation transfer, and (ii) we perform parameter transfer with a theoretical upper bound which is also label aware. We conduct extensive experiments on 12 cross-specialty NER tasks. The experimental results demonstrate that La-DTL provides consistent accuracy improvement over strong baselines. Besides, the promising experimental results on non-medical NER scenarios indicate that La-DTL is potential to be seamlessly adapted to a wide range of NER tasks.
Title: Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss
Venue: NAACL (2018)
Topic: information extraction
Abstract: The task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a hierarchy to entity mentions in text. Existing methods rely on distant supervision and are thus susceptible to noisy labels that can be out-of-context or overly-specific for the training sentence. Previous methods that attempt to address these issues do so with heuristics or with the help of hand-crafted features. Instead, we propose an end-to-end solution with a neural network model that uses a variant of cross-entropy loss function to handle out-of-context labels, and hierarchical loss normalization to cope with overly-specific ones. Also, previous work solve FETC a multi-label classification followed by ad-hoc post-processing. In contrast, our solution is more elegant: we use public word embeddings to train a single-label that jointly learns representations for entity mentions and their context. We show experimentally that our approach is robust against noise and consistently outperforms the state-of-the-art on established benchmarks for the task.
Title: Joint Bootstrapping Machines for High Confidence Relation Extraction
Venue: NAACL (2018)
Topic: information extraction
Abstract: Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed instances. Due to the lack of labeled data, a key challenge in bootstrapping is semantic drift: if a false positive instance is added during an iteration, then all following iterations are contaminated. We introduce BREX, a new bootstrapping method that protects against such contamination by highly effective confidence assessment. This is achieved by using entity and template seeds jointly (as opposed to just one as in previous work), by expanding entities and templates in parallel and in a mutually constraining fashion in each iteration and by introducing higherquality similarity measures for templates. Experimental results show that BREX achieves an F1 that is 0.13 (0.87 vs. 0.74) better than the state of the art for four relationships.
Title: A Deep Generative Model of Vowel Formant Typology
Venue: NAACL (2018)
Topic: phonology, morphology, and word segmentation
Abstract: What makes some types of languages more probable than others? For instance, we know that almost all spoken languages contain the vowel phoneme /i/; why should that be? The field of linguistic typology seeks to answer these questions and, thereby, divine the mechanisms that underlie human language. In our work, we tackle the problem of vowel system typology, i.e., we propose a generative probability model of which vowels a language contains. In contrast to previous work, we work directly with the acoustic information—the first two formant values—rather than modeling discrete sets of symbols from the international phonetic alphabet. We develop a novel generative probability model and report results on over 200 languages.
Title: Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages
Venue: NAACL (2018)
Topic: phonology, morphology, and word segmentation
Abstract: Morphological segmentation for polysynthetic languages is challenging, because a word may consist of many individual morphemes and training data can be extremely scarce. Since neural sequence-to-sequence (seq2seq) models define the state of the art for morphological segmentation in high-resource settings and for (mostly) European languages, we first show that they also obtain competitive performance for Mexican polysynthetic languages in minimal-resource settings. We then propose two novel multi-task training approaches—one with, one without need for external unlabeled resources—, and two corresponding data augmentation methods, improving over the neural baseline for all languages. Finally, we explore cross-lingual transfer as a third way to fortify our neural model and show that we can train one single multi-lingual model for related languages while maintaining comparable or even improved performance, thus reducing the amount of parameters by close to 75%. We provide our morphological segmentation datasets for Mexicanero, Nahuatl, Wixarika and Yorem Nokki for future research.
Title: Improving Character-Based Decoding Using Target-Side Morphological Information for Neural Machine Translation
Venue: NAACL (2018)
Topic: machine translation
Abstract: Recently, neural machine translation (NMT) has emerged as a powerful alternative to conventional statistical approaches. However, its performance drops considerably in the presence of morphologically rich languages (MRLs). Neural engines usually fail to tackle the large vocabulary and high out-of-vocabulary (OOV) word rate of MRLs. Therefore, it is not suitable to exploit existing word-based models to translate this set of languages. In this paper, we propose an extension to the state-of-the-art model of Chung et al. (2016), which works at the character level and boosts the decoder with target-side morphological information. In our architecture, an additional morphology table is plugged into the model. Each time the decoder samples from a target vocabulary, the table sends auxiliary signals from the most relevant affixes in order to enrich the decoder’s current state and constrain it to provide better predictions. We evaluated our model to translate English into German, Russian, and Turkish as three MRLs and observed significant improvements.
Title: Parsing Speech: a Neural Approach to Integrating Lexical and Acoustic-Prosodic Information
Venue: NAACL (2018)
Topic: linguistic theories, cognitive modeling and psycholinguistics
Abstract: In conversational speech, the acoustic signal provides cues that help listeners disambiguate difficult parses. For automatically parsing spoken utterances, we introduce a model that integrates transcribed text and acoustic-prosodic features using a convolutional neural network over energy and pitch trajectories coupled with an attention-based recurrent neural network that accepts text and prosodic features. We find that different types of acoustic-prosodic features are individually helpful, and together give statistically significant improvements in parse and disfluency detection F1 scores over a strong text-only baseline. For this study with known sentence boundaries, error analyses show that the main benefit of acoustic-prosodic features is in sentences with disfluencies, attachment decisions are most improved, and transcription errors obscure gains from prosody.
Title: Tied Multitask Learning for Neural Speech Translation
Venue: NAACL (2018)
Topic: speech processing and spoken language understanding
Abstract: We explore multitask models for neural translation of speech, augmenting them in order to reflect two intuitive notions. First, we introduce a model where the second task decoder receives information from the decoder of the first task, since higher-level intermediate representations should provide useful information. Second, we apply regularization that encourages transitivity and invertibility. We show that the application of these notions on jointly trained models improves performance on the tasks of low-resource speech transcription and translation. It also leads to better performance when using attention information for word discovery over unsegmented input.
Title: Please Clap: Modeling Applause in Campaign Speeches
Venue: NAACL (2018)
Topic: computational social science and cultural analytics
Abstract: This work examines the rhetorical techniques that speakers employ during political campaigns. We introduce a new corpus of speeches from campaign events in the months leading up to the 2016 U.S. presidential election and develop new models for predicting moments of audience applause. In contrast to existing datasets, we tackle the challenge of working with transcripts that derive from uncorrected closed captioning, using associated audio recordings to automatically extract and align labels for instances of audience applause. In prediction experiments, we find that lexical features carry the most information, but that a variety of features are predictive, including prosody, long-term contextual dependencies, and theoretically motivated features designed to capture rhetorical techniques.
Title: Attentive Interaction Model: Modeling Changes in View in Argumentation
Venue: NAACL (2018)
Topic: discourse and pragmatics
Abstract: We present a neural architecture for modeling argumentative dialogue that explicitly models the interplay between an Opinion Holder’s (OH’s) reasoning and a challenger’s argument, with the goal of predicting if the argument successfully changes the OH’s view. The model has two components: (1) vulnerable region detection, an attention model that identifies parts of the OH’s reasoning that are amenable to change, and (2) interaction encoding, which identifies the relationship between the content of the OH’s reasoning and that of the challenger’s argument. Based on evaluation on discussions from the Change My View forum on Reddit, the two components work together to predict an OH’s change in view, outperforming several baselines. A posthoc analysis suggests that sentences picked out by the attention model are addressed more frequently by successful arguments than by unsuccessful ones.
Title: Automatic Focus Annotation: Bringing Formal Pragmatics Alive in Analyzing the Information Structure of Authentic Data
Venue: NAACL (2018)
Topic: discourse and pragmatics
Abstract: Analyzing language in context, both from a theoretical and from a computational perspective, is receiving increased interest. Complementing the research in linguistics on discourse and information structure, in computational linguistics identifying discourse concepts was also shown to improve the performance of certain applications, for example, Short Answer Assessment systems (Ziai and Meurers, 2014). Building on the research that established detailed annotation guidelines for manual annotation of information structural concepts for written (Dipper et al., 2007; Ziai and Meurers, 2014) and spoken language data (Calhoun et al., 2010), this paper presents the first approach automating the analysis of focus in authentic written data. Our classification approach combines a range of lexical, syntactic, and semantic features to achieve an accuracy of 78.1% for identifying focus.