pwc-archive/papers-with-abstracts huggingface dataset:
Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog
Authors: Rashmi Gangadharaiah, Balakrishnan Narayanaswamy
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: General Classification, Goal-Oriented Dialog, intent-classification, Intent Classification, Intent Detection, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Sentence

Neural network models have recently gained traction for sentence-level intent classification and token-based slot-label identification. In many real-world scenarios, users have multiple intents in the same utterance, and a token-level slot label can belong to more than one intent. We investigate an attention-based neural network model that performs multi-label classification for identifying multiple intents and produces labels for both intents and slot-labels at the token-level. We show state-of-the-art performance for both intent detection and slot-label identification by comparing against strong, recently proposed models. Our model provides a small but statistically significant improvement of 0.2{\%} on the predominantly single-intent ATIS public data set, and 55{\%} intent accuracy improvement on an internal multi-intent dataset.
Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling
Authors: Meishan Zhang, Peili Liang, Guohong Fu
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Fine-Grained Opinion Analysis, Opinion Mining, Semantic Role Labeling

Opinion role labeling (ORL) is an important task for fine-grained opinion mining, which identifies important opinion arguments such as holder and target for a given opinion trigger. The task is highly correlative with semantic role labeling (SRL), which identifies important semantic arguments such as agent and patient for a given predicate. As predicate agents and patients usually correspond to opinion holders and targets respectively, SRL could be valuable for ORL. In this work, we propose a simple and novel method to enhance ORL by utilizing SRL, presenting semantic-aware word representations which are learned from SRL. The representations are then fed into a baseline neural ORL model as basic inputs. We verify the proposed method on a benchmark MPQA corpus. Experimental results show that the proposed method is highly effective. In addition, we compare the method with two representative methods of SRL integration as well, finding that our method can outperform the two methods significantly, achieving 1.47{\%} higher F-scores than the better one.
Generalizing Unmasking for Short Texts
Authors: Janek Bevendorff, Benno Stein, Matthias Hagen, Martin Potthast
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Authorship Verification, General Classification

Authorship verification is the problem of inferring whether two texts were written by the same author. For this task, unmasking is one of the most robust approaches as of today with the major shortcoming of only being applicable to book-length texts. In this paper, we present a generalized unmasking approach which allows for authorship verification of texts as short as four printed pages with very high precision at an adjustable recall tradeoff. Our generalized approach therefore reduces the required material by orders of magnitude, making unmasking applicable to authorship cases of more practical proportions. The new approach is on par with other state-of-the-art techniques that are optimized for texts of this length: it achieves accuracies of 75{--}80{\%}, while also allowing for easy adjustment to forensic scenarios that require higher levels of confidence in the classification.
Keyphrase Generation: A Text Summarization Struggle
Authors: Erion {\c{C}}ano, Ond{\v{r}}ej Bojar
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Articles, Keyphrase Generation, Text Summarization

Authors{'} keyphrases assigned to scientific articles are essential for recognizing content and topic aspects. Most of the proposed supervised and unsupervised methods for keyphrase generation are unable to produce terms that are valuable but do not appear in the text. In this paper, we explore the possibility of considering the keyphrase string as an abstractive summary of the title and the abstract. First, we collect, process and release a large dataset of scientific paper metadata that contains 2.2 million records. Then we experiment with popular text summarization neural architectures. Despite using advanced deep learning models, large quantities of data and many days of computation, our systematic evaluation on four test datasets reveals that the explored text summarization methods could not produce better keyphrases than the simpler unsupervised methods, or the existing supervised ones.
SEQ\^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression
Authors: Christos Baziotis, Ion Androutsopoulos, Ioannis Konstas, Alex Potamianos, ros
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Decoder, Language Modeling, Language Modelling, Reinforcement Learning, Sentence, Sentence Compression, Unsupervised Sentence Compression

Neural sequence-to-sequence models are currently the dominant approach in several natural language processing tasks, but require large parallel corpora. We present a sequence-to-sequence-to-sequence autoencoder (SEQ{\^{}}3), consisting of two chained encoder-decoder pairs, with words used as a sequence of discrete latent variables. We apply the proposed model to unsupervised abstractive sentence compression, where the first and last sequences are the input and reconstructed sentences, respectively, while the middle sequence is the compressed sentence. Constraining the length of the latent word sequences forces the model to distill important information from the input. A pretrained language model, acting as a prior over the latent sequences, encourages the compressed sentences to be human-readable. Continuous relaxations enable us to sample from categorical distributions, allowing gradient-based optimization, unlike alternatives that rely on reinforcement learning. The proposed model does not require parallel text-summary pairs, achieving promising results in unsupervised sentence compression on benchmark datasets.
Serial Recall Effects in Neural Language Modeling
Authors: Hassan Hajipoor, Hadi Amiri, Maseud Rahgozar, Farhad Oroumchian
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Language Modeling, Language Modelling

Serial recall experiments study the ability of humans to recall words in the order in which they occurred. The following serial recall effects are generally investigated in studies with humans: word length and frequency, primacy and recency, semantic confusion, repetition, and transposition effects. In this research, we investigate neural language models in the context of these serial recall effects. Our work provides a framework to better understand and analyze neural language models and opens a new window to develop accurate language models.
Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization
Authors: Tobias Falke, Iryna Gurevych
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Clustering, Document Summarization, Multi-Document Summarization

Concept map-based multi-document summarization has recently been proposed as a variant of the traditional summarization task with graph-structured summaries. As shown by previous work, the grouping of coreferent concept mentions across documents is a crucial subtask of it. However, while the current state-of-the-art method suggested a new grouping method that was shown to improve the summary quality, its use of pairwise comparisons leads to polynomial runtime complexity that prohibits the application to large document collections. In this paper, we propose two alternative grouping techniques based on locality sensitive hashing, approximate nearest neighbor search and a fast clustering algorithm. They exhibit linear and log-linear runtime complexity, making them much more scalable. We report experimental results that confirm the improved runtime behavior while also showing that the quality of the summary concept maps remains comparable.
Left-to-Right Dependency Parsing with Pointer Networks
Authors: Daniel Fern{\'a}ndez-Gonz{\'a}lez, Carlos G{\'o}mez-Rodr{\'\i}guez
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Dependency Parsing, Sentence

We propose a novel transition-based algorithm that straightforwardly parses sentences from left to right by building n attachments, with n being the length of the input sentence. Similarly to the recent stack-pointer parser by Ma et al. (2018), we use the pointer network framework that, given a word, can directly point to a position from the sentence. However, our left-to-right approach is simpler than the original top-down stack-pointer parser (not requiring a stack) and reduces transition sequence length in half, from 2n-1 actions to n. This results in a quadratic non-projective parser that runs twice as fast as the original while achieving the best accuracy to date on the English PTB dataset (96.04{\%} UAS, 94.43{\%} LAS) among fully-supervised single-model dependency parsers, and improves over the former top-down transition system in the majority of languages tested.
Pooled Contextualized Embeddings for Named Entity Recognition
Authors: Alan Akbik, Tanja Bergmann, Rol Vollgraf
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Contextual string embeddings are a recent type of contextualized word embedding that were shown to yield state-of-the-art results when utilized in a range of sequence labeling tasks. They are based on character-level language models which treat text as distributions over characters and are capable of generating embeddings for any string of characters within any textual context. However, such purely character-based approaches struggle to produce meaningful embeddings if a rare string is used in a underspecified context. To address this drawback, we propose a method in which we dynamically aggregate contextualized embeddings of each unique string that we encounter. We then use a pooling operation to distill a {''}global{''} word representation from all contextualized instances. We evaluate these {''}pooled contextualized embeddings{''} on common named entity recognition (NER) tasks such as CoNLL-03 and WNUT and show that our approach significantly improves the state-of-the-art for NER. We make all code and pre-trained models available to the research community for use and reproduction.
Better Modeling of Incomplete Annotations for Named Entity Recognition
Authors: Zhanming Jie, Pengjun Xie, Wei Lu, Ruixue Ding, Linlin Li
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Supervised approaches to named entity recognition (NER) are largely developed based on the assumption that the training data is fully annotated with named entity information. However, in practice, annotated data can often be imperfect with one typical issue being the training data may contain incomplete annotations. We highlight several pitfalls associated with learning under such a setup in the context of NER and identify limitations associated with existing approaches, proposing a novel yet easy-to-implement approach for recognizing named entities with incomplete data annotations. We demonstrate the effectiveness of our approach through extensive experiments.
Event Detection without Triggers
Authors: Shulin Liu, Yang Li, Feng Zhang, Tao Yang, Xinpeng Zhou
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Event Detection, Sentence

The goal of event detection (ED) is to detect the occurrences of events and categorize them. Previous work solved this task by recognizing and classifying event triggers, which is defined as the word or phrase that most clearly expresses an event occurrence. As a consequence, existing approaches required both annotated triggers and event types in training data. However, triggers are nonessential to event detection, and it is time-consuming for annotators to pick out the {``}most clearly{''} word from a given sentence, especially from a long sentence. The expensive annotation of training corpus limits the application of existing approaches. To reduce manual effort, we explore detecting events without triggers. In this work, we propose a novel framework dubbed as Type-aware Bias Neural Network with Attention Mechanisms (TBNNAM), which encodes the representation of a sentence based on target event types. Experimental results demonstrate the effectiveness. Remarkably, the proposed approach even achieves competitive performances compared with state-of-the-arts that used annotated triggers.
Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data
Authors: Yin Jou Huang, Jing Lu, Sadao Kurohashi, Vincent Ng
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: coreference-resolution, Coreference Resolution, Event Coreference Resolution, Transfer Learning

Argument compatibility is a linguistic condition that is frequently incorporated into modern event coreference resolution systems. If two event mentions have incompatible arguments in any of the argument roles, they cannot be coreferent. On the other hand, if these mentions have compatible arguments, then this may be used as information towards deciding their coreferent status. One of the key challenges in leveraging argument compatibility lies in the paucity of labeled data. In this work, we propose a transfer learning framework for event coreference resolution that utilizes a large amount of unlabeled data to learn argument compatibility of event mentions. In addition, we adopt an interactive inference network based model to better capture the compatible and incompatible relations between the context words of event mentions. Our experiments on the KBP 2017 English dataset confirm the effectiveness of our model in learning argument compatibility, which in turn improves the performance of the overall event coreference model.
Description-Based Zero-shot Fine-Grained Entity Typing
Authors: Rasha Obeidat, Xiaoli Fern, Hamed Shahbazi, Prasad Tadepalli
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Entity Typing, Vocal Bursts Type Prediction

Fine-grained Entity typing (FGET) is the task of assigning a fine-grained type from a hierarchy to entity mentions in the text. As the taxonomy of types evolves continuously, it is desirable for an entity typing system to be able to recognize novel types without additional training. This work proposes a zero-shot entity typing approach that utilizes the type description available from Wikipedia to build a distributed semantic representation of the types. During training, our system learns to align the entity mentions and their corresponding type representations on the known types. At test time, any new type can be incorporated into the system given its Wikipedia descriptions. We evaluate our approach on FIGER, a public benchmark entity tying dataset. Because the existing test set of FIGER covers only a small portion of the fine-grained types, we create a new test set by manually annotating a portion of the noisy training data. Our experiments demonstrate the effectiveness of the proposed method in recognizing novel types that are not present in the training data.
Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting
Authors: J. Edward Hu, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, Benjamin Van Durme
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Data Augmentation, Machine Translation, Natural Language Inference, Question Answering, Translation

Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in generation tasks such as machine translation or monolingual text rewriting. We describe vectorized dynamic beam allocation, which extends work in lexically-constrained decoding to work with batching, leading to a five-fold improvement in throughput when working with positive constraints. Faster decoding enables faster exploration of constraint strategies: we illustrate this via data augmentation experiments with a monolingual rewriter applied to the tasks of natural language inference, question answering and machine translation, showing improvements in all three.
Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network
Authors: Hitesh Golchha, Mauajama Firdaus, Asif Ekbal, Pushpak Bhattacharyya
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6

In this paper, we propose an effective deep learning framework for inducing courteous behavior in customer care responses. The interaction between a customer and the customer care representative contributes substantially to the overall customer experience. Thus it is imperative for customer care agents and chatbots engaging with humans to be personal, cordial and emphatic to ensure customer satisfaction and retention. Our system aims at automatically transforming neutral customer care responses into courteous replies. Along with stylistic transfer (of courtesy), our system ensures that responses are coherent with the conversation history, and generates courteous expressions consistent with the emotional state of the customer. Our technique is based on a reinforced pointer-generator model for the sequence to sequence task. The model is also conditioned on a hierarchically encoded and emotionally aware conversational context. We use real interactions on Twitter between customer care professionals and aggrieved customers to create a large conversational dataset having both forms of agent responses: {`}generic{'} and {`}courteous{'}. We perform quantitative and qualitative analyses on established and task-specific metrics, both automatic and human evaluation based. Our evaluation shows that the proposed models can generate emotionally-appropriate courteous expressions while preserving the content. Experimental results also prove that our proposed approach performs better than the baseline models.
How to Avoid Sentences Spelling Boring? Towards a Neural Approach to Unsupervised Metaphor Generation
Authors: Zhiwei Yu, Xiaojun Wan
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Language Modeling, Language Modelling, Text Generation

Metaphor generation attempts to replicate human creativity with language, which is an attractive but challengeable text generation task. Previous efforts mainly focus on template-based or rule-based methods and result in a lack of linguistic subtlety. In order to create novel metaphors, we propose a neural approach to metaphor generation and explore the shared inferential structure of a metaphorical usage and a literal usage of a verb. Our approach does not require any manually annotated metaphors for training. We extract the metaphorically used verbs with their metaphorical senses in an unsupervised way and train a neural language model from wiki corpus. Then we generate metaphors conveying the assigned metaphorical senses with an improved decoding algorithm. Automatic metrics and human evaluations demonstrate that our approach can generate metaphors with good readability and creativity.
Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process
Authors: Dingcheng Li, Siamak Zamani, Jingyuan Zhang, Ping Li
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Document Classification, General Classification, Graph Embedding, Knowledge Graph Embedding, Topic Models, Variational Inference

Leveraging domain knowledge is an effective strategy for enhancing the quality of inferred low-dimensional representations of documents by topic models. In this paper, we develop \textit{topic modeling with knowledge graph embedding} (TMKGE), a Bayesian nonparametric model to employ knowledge graph (KG) embedding in the context of topic modeling, for extracting more coherent topics. Specifically, we build a hierarchical Dirichlet process (HDP) based model to flexibly borrow information from KG to improve the interpretability of topics. An efficient online variational inference method based on a stick-breaking construction of HDP is developed for TMKGE, making TMKGE suitable for large document corpora and KGs. Experiments on three public datasets illustrate the superior performance of TMKGE in terms of topic coherence and document classification accuracy, compared to state-of-the-art topic modeling methods.
Adaptive Convolution for Multi-Relational Learning
Authors: Xiaotian Jiang, Quan Wang, Bin Wang
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Link Prediction, Relation, Relational Reasoning

We consider the problem of learning distributed representations for entities and relations of multi-relational data so as to predict missing links therein. Convolutional neural networks have recently shown their superiority for this problem, bringing increased model expressiveness while remaining parameter efficient. Despite the success, previous convolution designs fail to model full interactions between input entities and relations, which potentially limits the performance of link prediction. In this work we introduce ConvR, an adaptive convolutional network designed to maximize entity-relation interactions in a convolutional fashion. ConvR adaptively constructs convolution filters from relation representations, and applies these filters across entity representations to generate convolutional features. As such, ConvR enables rich interactions between entity and relation representations at diverse regions, and all the convolutional features generated will be able to capture such interactions. We evaluate ConvR on multiple benchmark datasets. Experimental results show that: (1) ConvR performs substantially better than competitive baselines in almost all the metrics and on all the datasets; (2) Compared with state-of-the-art convolutional models, ConvR is not only more effective but also more efficient. It offers a 7{\%} increase in MRR and a 6{\%} increase in Hits@10, while saving 12{\%} in parameter storage.
Adversarial Training for Weakly Supervised Event Detection
Authors: Xiaozhi Wang, Xu Han, Zhiyuan Liu, Maosong Sun, Peng Li
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Event Detection

Modern weakly supervised methods for event detection (ED) avoid time-consuming human annotation and achieve promising results by learning from auto-labeled data. However, these methods typically rely on sophisticated pre-defined rules as well as existing instances in knowledge bases for automatic annotation and thus suffer from low coverage, topic bias, and data noise. To address these issues, we build a large event-related candidate set with good coverage and then apply an adversarial training mechanism to iteratively identify those informative instances from the candidate set and filter out those noisy ones. The experiments on two real-world datasets show that our candidate selection and adversarial training can cooperate together to obtain more diverse and accurate training data for ED, and significantly outperform the state-of-the-art methods in various weakly supervised scenarios. The datasets and source code can be obtained from https://github.com/thunlp/Adv-ED.