Proceedings of the 2019 Conference of the North
Is It Dish Washer Safe? Automatically Answering “Yes/No” Questions Using Customer Reviews (N19-3)
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| Challenge: | Using Amazon reviews, we find that the answer to a question is only in 45% of cases. |
| Approach: | They combine Amazon reviews with consumer reviews and manually analyse 400 questions from four domains to find that reviews directly contain the answer to the question . they then compare QA systems that use reviews in addition to the questions to see if they can be useful for other question types. |
| Outcome: | The proposed system outperforms the chance baseline but not by a large margin. |
Identifying and Reducing Gender Bias in Word-Level Language Models (N19-3)
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| Challenge: | Existing discriminatory biases in training data can be amplified by models . text corpora exhibit socially problematic biase . |
| Approach: | They propose a metric to measure gender bias and a regularization loss term to minimize embeddings onto an embeddable subspace that encodes gender. |
| Outcome: | The proposed method reduces gender bias up to an optimal weight assigned to the loss term, and the model becomes unstable as the perplexity increases. |
Emotion Impacts Speech Recognition Performance (N19-3)
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| Challenge: | Existing studies show that speech recognition systems depend on multiple factors including lexical content, speaker identity and dialect. |
| Approach: | They propose a method that evaluates the impact of emotion on recognition even when manual transcripts are not available. |
| Outcome: | The proposed method allows to evaluate the impact of emotion on recognition even when manual transcripts are not available. |
The Strength of the Weakest Supervision: Topic Classification Using Class Labels (N19-3)
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| Challenge: | a topic classifier can understand only class labels when training for tasks that require a large amount of labeled documents. |
| Approach: | They propose an algorithm that can initialize a topic classifier using only class labels . they propose a method that combines word embedding and naive Bayes classification . |
| Outcome: | The proposed approach saves significant initial labeling effort by providing a "warm start" the proposed approach can be fine-tuned with more labeled documents to reach a certain performance level. |
Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling (N19-3)
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| Challenge: | In low-resource environments, self-training is less effective due to unreliable annotations . we combine self-teaching with noise handling to clean the self-labeled data . |
| Approach: | They propose to combine self-training with noise handling to clean unlabeled data . they propose to model clean and noisy labels separately to improve performance . |
| Outcome: | The proposed method performs better than baseline methods on Chunking and NER. |
Opinion Mining with Deep Contextualized Embeddings (N19-3)
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| Challenge: | Existing methods for opinion expression detection are based on token-level sequence labeling . |
| Approach: | They propose to use BERT and conditional random field embedders to detect opinion expressions. |
| Outcome: | The proposed model outperforms ELMo embedders in opinion expression detection. |
A Bag-of-concepts Model Improves Relation Extraction in a Narrow Knowledge Domain with Limited Data (N19-3)
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| Challenge: | Existing methods for relation extraction on small data sets are time-consuming and expensive. |
| Approach: | They propose an automatic relation extraction task with limited annotated data and a narrow knowledge domain. |
| Outcome: | The proposed method outperforms methods of higher complexity on a small clinical corpus. |
Generating Text through Adversarial Training Using Skip-Thought Vectors (N19-3)
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| Challenge: | Existing approaches to use word embeddings for text generation have been limited. |
| Approach: | They propose to use GANs with word embeddings to reproduce writing style in text . they use a sentence embeddable vector to model people's way of expression . |
| Outcome: | The proposed model outperforms baseline text generation networks across several metrics including BLEU-n, METEOR and ROUGE. |
A Partially Rule-Based Approach to AMR Generation (N19-3)
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| Challenge: | Abstract Meaning Representation (AMR) is a representation of a sentence as a labeled graph . because of these abstractions, it can be difficult to generate from AMR back to a fluent English sentence . |
| Approach: | They propose a new approach to generating English text from Abstract Meaning Representation (AMR) it is largely rule-based, supplemented by a language model and simple statistical linearization models . they also address difficulties of automatically evaluating AMR generation systems . |
| Outcome: | The proposed approach produces a fluent English sentence with a high quality . it is difficult to generate from an AMR back to a sentence which preserves original meaning . |
Computational Investigations of Pragmatic Effects in Natural Language (N19-3)
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| Challenge: | a recent paper examines the relationship between semantics and pragmatics in language. |
| Approach: | They propose to develop computational models that leverage pragmatic knowledge in language . goal is to build better and more pragmatically-aware natural language generation and understanding systems . |
| Outcome: | The proposed models leverage pragmatic knowledge in language crucial to performing many NLP tasks correctly. |
SEDTWik: Segmentation-based Event Detection from Tweets Using Wikipedia (N19-3)
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| Challenge: | Recent work on event detection from tweets has focused on localized events or breaking news only. |
| Approach: | They propose to split tweets into segments, extract bursty segments, cluster them, summarize them. |
| Outcome: | The proposed system can detect newsworthy events occurring at different locations of the world from a wide range of categories. |
Multimodal Machine Translation with Embedding Prediction (N19-3)
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| Challenge: | Pretrained word embeddings improve multimodal machine translation of low-resource domains due to a shortage of training data. |
| Approach: | They propose to combine pretrained word embeddings with search-based approaches to improve NMT of low-resource domains to better translate rare words. |
| Outcome: | The proposed approach improves translation performance by 1.24 METEOR and 2.49 BLEU and achieves 7.67 F-score. |
Deep Learning and Sociophonetics: Automatic Coding of Rhoticity Using Neural Networks (N19-3)
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| Challenge: | Automated extraction methods for vowels are available, but coding rhoticity has lagged behind. |
| Approach: | They use Neural Networks/Deep Learning to train a model on 208 speakers in Boston . they find that there is no reliable method for classifying r-dropping . |
| Outcome: | The proposed method trains a model on 208 speakers in Boston, Massachusetts. |
Data Augmentation by Data Noising for Open-vocabulary Slots in Spoken Language Understanding (N19-3)
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| Challenge: | Neural networks are used to understand spoken language understanding (SLU) but it is difficult to recognize the slots of unknown words or ‘open-vocabulary’ slots because of the high cost of creating a manually tagged SLU dataset. |
| Approach: | They propose to use a recurrent neural network to nois slots for data augmentation by using an attention-based bi-directional recurrence neural network. |
| Outcome: | The proposed method achieves performance improvements of up to 0.57% and 3.25 in intent prediction (accuracy) and slot filling (f1-score) and 0.53% accuracy. |
Expectation and Locality Effects in the Prediction of Disfluent Fillers and Repairs in English Speech (N19-3)
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| Challenge: | a study aims to understand the role of disfluencies in speech production . speakers tend to lessen cognitive load for upcoming difficulties . |
| Approach: | They examine the role of three influential theories of language processing in predicting disfluencies in speech production. |
| Outcome: | The proposed classifiers predict disfluencies in English conversational speech . the classifier features lexical surprisal, word duration and DLT integration costs . |
Gating Mechanisms for Combining Character and Word-level Word Representations: an Empirical Study (N19-3)
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| Challenge: | Existing studies show that combining character and word-level representations improves word and sentence representations . however, word-based embeddings do not account for derivational processes resulting in syntactically-similar words with different meanings. |
| Approach: | They propose to combine character and word-level representations to improve word and sentence representations. |
| Outcome: | The proposed method performed well in several word similarity datasets. |
A Pregroup Representation of Word Order Alternation Using Hindi Syntax (N19-3)
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| Challenge: | Existing methods for expressing restricted word order alternation have not been used for word order representations. |
| Approach: | They propose three methods to represent restricted word order alternation in the pregroup representation of any language. |
| Outcome: | The proposed methods are able to represent word alternation in English using the example of Hindi syntax. |
Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment (N19-3)
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| Challenge: | #MeToo movement provides platform to narrate personal experiences of sexual harassment. |
| Approach: | They propose a three-part ULMFiT architecture to tackle text subtleties in a classification task . they propose to annotate a manually annotated real-world dataset to test their approach . |
| Outcome: | The proposed model outperforms existing models that rely on handcrafted stylistic features and is more accurate than generic models. |
SNAP-BATNET: Cascading Author Profiling and Social Network Graphs for Suicide Ideation Detection on Social Media (N19-3)
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Rohan Mishra, Pradyumn Prakhar Sinha, Ramit Sawhney, Debanjan Mahata, Puneet Mathur, Rajiv Ratn Shah
| Challenge: | Suicide is a leading cause of death among youth worldwide and currently only uses text-based cues to detect suicidal ideation. |
| Approach: | They propose a deep learning based model to extract text-based features from tweets and a novel Feature Stacking approach to combine other community-based information. |
| Outcome: | The proposed model outperforms existing models on an annotated dataset of tweets using a three-phase strategy and proposes a novel Feature Stacking approach to combine other community-based information such as historical author profiling and graph embeddings. |