yuz9yuz/NLPImpact huggingface dataset:
Title: Pun Generation with Surprise
Venue: NAACL (2019)
Topic: resources and evaluation
Abstract: We tackle the problem of generating a pun sentence given a pair of homophones (e.g., “died” and “dyed”). Puns are by their very nature statistically anomalous and not amenable to most text generation methods that are supervised by a large corpus. In this paper, we propose an unsupervised approach to pun generation based on lots of raw (unhumorous) text and a surprisal principle. Specifically, we posit that in a pun sentence, there is a strong association between the pun word (e.g., “dyed”) and the distant context, but a strong association between the alternative word (e.g., “died”) and the immediate context. We instantiate the surprisal principle in two ways: (i) as a measure based on the ratio of probabilities given by a language model, and (ii) a retrieve-and-edit approach based on words suggested by a skip-gram model. Based on human evaluation, our retrieve-and-edit approach generates puns successfully 30% of the time, doubling the success rate of a neural generation baseline.
Title: Single Document Summarization as Tree Induction
Venue: NAACL (2019)
Topic: information extraction
Abstract: In this paper, we conceptualize single-document extractive summarization as a tree induction problem. In contrast to previous approaches which have relied on linguistically motivated document representations to generate summaries, our model induces a multi-root dependency tree while predicting the output summary. Each root node in the tree is a summary sentence, and the subtrees attached to it are sentences whose content relates to or explains the summary sentence. We design a new iterative refinement algorithm: it induces the trees through repeatedly refining the structures predicted by previous iterations. We demonstrate experimentally on two benchmark datasets that our summarizer performs competitively against state-of-the-art methods.
Title: Fixed That for You: Generating Contrastive Claims with Semantic Edits
Venue: NAACL (2019)
Topic: interpretability and analysis of models for nlp
Abstract: Understanding contrastive opinions is a key component of argument generation. Central to an argument is the claim, a statement that is in dispute. Generating a counter-argument then requires generating a response in contrast to the main claim of the original argument. To generate contrastive claims, we create a corpus of Reddit comment pairs self-labeled by posters using the acronym FTFY (fixed that for you). We then train neural models on these pairs to edit the original claim and produce a new claim with a different view. We demonstrate significant improvement over a sequence-to-sequence baseline in BLEU score and a human evaluation for fluency, coherence, and contrast.
Title: Box of Lies: Multimodal Deception Detection in Dialogues
Venue: NAACL (2019)
Topic: resources and evaluation
Abstract: Deception often takes place during everyday conversations, yet conversational dialogues remain largely unexplored by current work on automatic deception detection. In this paper, we address the task of detecting multimodal deceptive cues during conversational dialogues. We introduce a multimodal dataset containing deceptive conversations between participants playing the Box of Lies game from The Tonight Show Starring Jimmy Fallon, in which they try to guess whether an object description provided by their opponent is deceptive or not. We conduct annotations of multimodal communication behaviors, including facial and linguistic behaviors, and derive several learning features based on these annotations. Initial classification experiments show promising results, performing well above both a random and a human baseline, and reaching up to 69% accuracy in distinguishing deceptive and truthful behaviors.
Title: A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation
Venue: NAACL (2019)
Topic: discourse and pragmatics
Abstract: We present a corpus of anaphoric information (coreference) crowdsourced through a game-with-a-purpose. The corpus, containing annotations for about 108,000 markables, is one of the largest corpora for coreference for English, and one of the largest crowdsourced NLP corpora, but its main feature is the large number of judgments per markable: 20 on average, and over 2.2M in total. This characteristic makes the corpus a unique resource for the study of disagreements on anaphoric interpretation. A second distinctive feature is its rich annotation scheme, covering singletons, expletives, and split-antecedent plurals. Finally, the corpus also comes with labels inferred using a recently proposed probabilistic model of annotation for coreference. The labels are of high quality and make it possible to successfully train a state of the art coreference resolver, including training on singletons and non-referring expressions. The annotation model can also result in more than one label, or no label, being proposed for a markable, thus serving as a baseline method for automatically identifying ambiguous markables. A preliminary analysis of the results is presented.
Title: A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd
Venue: NAACL (2019)
Topic: discourse and pragmatics
Abstract: The study of argumentation and the development of argument mining tools depends on the availability of annotated data, which is challenging to obtain in sufficient quantity and quality. We present a method that breaks down a popular but relatively complex discourse-level argument annotation scheme into a simpler, iterative procedure that can be applied even by untrained annotators. We apply this method in a crowdsourcing setup and report on the reliability of the annotations obtained. The source code for a tool implementing our annotation method, as well as the sample data we obtained (4909 gold-standard annotations across 982 documents), are freely released to the research community. These are intended to serve the needs of qualitative research into argumentation, as well as of data-driven approaches to argument mining.
Title: Unsupervised Dialog Structure Learning
Venue: NAACL (2019)
Topic: dialogue and interactive systems
Abstract: Learning a shared dialog structure from a set of task-oriented dialogs is an important challenge in computational linguistics. The learned dialog structure can shed light on how to analyze human dialogs, and more importantly contribute to the design and evaluation of dialog systems. We propose to extract dialog structures using a modified VRNN model with discrete latent vectors. Different from existing HMM-based models, our model is based on variational-autoencoder (VAE). Such model is able to capture more dynamics in dialogs beyond the surface forms of the language. We find that qualitatively, our method extracts meaningful dialog structure, and quantitatively, outperforms previous models on the ability to predict unseen data. We further evaluate the model’s effectiveness in a downstream task, the dialog system building task. Experiments show that, by integrating the learned dialog structure into the reward function design, the model converges faster and to a better outcome in a reinforcement learning setting.
Title: Modeling Document-level Causal Structures for Event Causal Relation Identification
Venue: NAACL (2019)
Topic: information extraction
Abstract: We aim to comprehensively identify all the event causal relations in a document, both within a sentence and across sentences, which is important for reconstructing pivotal event structures. The challenges we identified are two: 1) event causal relations are sparse among all possible event pairs in a document, in addition, 2) few causal relations are explicitly stated. Both challenges are especially true for identifying causal relations between events across sentences. To address these challenges, we model rich aspects of document-level causal structures for achieving comprehensive causal relation identification. The causal structures include heavy involvements of document-level main events in causal relations as well as several types of fine-grained constraints that capture implications from certain sentential syntactic relations and discourse relations as well as interactions between event causal relations and event coreference relations. Our experimental results show that modeling the global and fine-grained aspects of causal structures using Integer Linear Programming (ILP) greatly improves the performance of causal relation identification, especially in identifying cross-sentence causal relations.
Title: Hierarchical User and Item Representation with Three-Tier Attention for Recommendation
Venue: NAACL (2019)
Topic: nlp applications
Abstract: Utilizing reviews to learn user and item representations is useful for recommender systems. Existing methods usually merge all reviews from the same user or for the same item into a long document. However, different reviews, sentences and even words usually have different informativeness for modeling users and items. In this paper, we propose a hierarchical user and item representation model with three-tier attention to learn user and item representations from reviews for recommendation. Our model contains three major components, i.e., a sentence encoder to learn sentence representations from words, a review encoder to learn review representations from sentences, and a user/item encoder to learn user/item representations from reviews. In addition, we incorporate a three-tier attention network in our model to select important words, sentences and reviews. Besides, we combine the user and item representations learned from the reviews with user and item embeddings based on IDs as the final representations to capture the latent factors of individual users and items. Extensive experiments on four benchmark datasets validate the effectiveness of our approach.
Title: Text Similarity Estimation Based on Word Embeddings and Matrix Norms for Targeted Marketing
Venue: NAACL (2019)
Topic: computational social science and cultural analytics
Abstract: The prevalent way to estimate the similarity of two documents based on word embeddings is to apply the cosine similarity measure to the two centroids obtained from the embedding vectors associated with the words in each document. Motivated by an industrial application from the domain of youth marketing, where this approach produced only mediocre results, we propose an alternative way of combining the word vectors using matrix norms. The evaluation shows superior results for most of the investigated matrix norms in comparison to both the classical cosine measure and several other document similarity estimates.
Title: Glocal: Incorporating Global Information in Local Convolution for Keyphrase Extraction
Venue: NAACL (2019)
Topic: information extraction
Abstract: Graph Convolutional Networks (GCNs) are a class of spectral clustering techniques that leverage localized convolution filters to perform supervised classification directly on graphical structures. While such methods model nodes’ local pairwise importance, they lack the capability to model global importance relative to other nodes of the graph. This causes such models to miss critical information in tasks where global ranking is a key component for the task, such as in keyphrase extraction. We address this shortcoming by allowing the proper incorporation of global information into the GCN family of models through the use of scaled node weights. In the context of keyphrase extraction, incorporating global random walk scores obtained from TextRank boosts performance significantly. With our proposed method, we achieve state-of-the-art results, bettering a strong baseline by an absolute 2% increase in F1 score.
Title: A Study of Latent Structured Prediction Approaches to Passage Reranking
Venue: NAACL (2019)
Topic: information retrieval and text mining
Abstract: The structured output framework provides a helpful tool for learning to rank problems. In this paper, we propose a structured output approach which regards rankings as latent variables. Our approach addresses the complex optimization of Mean Average Precision (MAP) ranking metric. We provide an inference procedure to find the max-violating ranking based on the decomposition of the corresponding loss. The results of our experiments on WikiQA and TREC13 datasets show that our reranking based on structured prediction is a promising research direction.
Title: Combining Distant and Direct Supervision for Neural Relation Extraction
Venue: NAACL (2019)
Topic: information extraction
Abstract: In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism that attends to sentences that are likely to express the relations. We improve such models by combining the distant supervision data with an additional directly-supervised data, which we use as supervision for the attention weights. We find that joint training on both types of supervision leads to a better model because it improves the model’s ability to identify noisy sentences. In addition, we find that sigmoidal attention weights with max pooling achieves better performance over the commonly used weighted average attention in this setup. Our proposed method achieves a new state-of-the-art result on the widely used FB-NYT dataset.
Title: Tweet Stance Detection Using an Attention based Neural Ensemble Model
Venue: NAACL (2019)
Topic: computational social science and cultural analytics
Abstract: Stance detection in twitter aims at mining user stances expressed in a tweet towards a single or multiple target entities. To tackle this problem, most of the prior studies have been explored the traditional deep learning models, e.g., LSTM and GRU. However, in compared to these traditional approaches, recently proposed densely connected Bi-LSTM and nested LSTMs architectures effectively address the vanishing-gradient and overfitting problems as well as dealing with long-term dependencies. In this paper, we propose a neural ensemble model that adopts the strengths of these two LSTM variants to learn better long-term dependencies, where each module coupled with an attention mechanism that amplifies the contribution of important elements in the final representation. We also employ a multi-kernel convolution on top of them to extract the higher-level tweet representations. Results of extensive experiments on single and multi-target stance detection datasets show that our proposed method achieves substantial improvement over the current state-of-the-art deep learning based methods.
Title: Word Embedding-Based Automatic MT Evaluation Metric using Word Position Information
Venue: NAACL (2019)
Topic: machine translation
Abstract: We propose a new automatic evaluation metric for machine translation. Our proposed metric is obtained by adjusting the Earth Mover’s Distance (EMD) to the evaluation task. The EMD measure is used to obtain the distance between two probability distributions consisting of some signatures having a feature and a weight. We use word embeddings, sentence-level tf-idf, and cosine similarity between two word embeddings, respectively, as the features, weight, and the distance between two features. Results show that our proposed metric can evaluate machine translation based on word meaning. Moreover, for distance, cosine similarity and word position information are used to address word-order differences. We designate this metric as Word Embedding-Based automatic MT evaluation using Word Position Information (WE_WPI). A meta-evaluation using WMT16 metrics shared task set indicates that our WE_WPI achieves the highest correlation with human judgment among several representative metrics.
Title: Learning to Stop in Structured Prediction for Neural Machine Translation
Venue: NAACL (2019)
Topic: machine translation
Abstract: Beam search optimization (Wiseman and Rush, 2016) resolves many issues in neural machine translation. However, this method lacks principled stopping criteria and does not learn how to stop during training, and the model naturally prefers longer hypotheses during the testing time in practice since they use the raw score instead of the probability-based score. We propose a novel ranking method which enables an optimal beam search stop- ping criteria. We further introduce a structured prediction loss function which penalizes suboptimal finished candidates produced by beam search during training. Experiments of neural machine translation on both synthetic data and real languages (German→English and Chinese→English) demonstrate our pro- posed methods lead to better length and BLEU score.
Title: Learning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs
Venue: NAACL (2019)
Topic: multilinguality and language diversity
Abstract: Recent research has discovered that a shared bilingual word embedding space can be induced by projecting monolingual word embedding spaces from two languages using a self-learning paradigm without any bilingual supervision. However, it has also been shown that for distant language pairs such fully unsupervised self-learning methods are unstable and often get stuck in poor local optima due to reduced isomorphism between starting monolingual spaces. In this work, we propose a new robust framework for learning unsupervised multilingual word embeddings that mitigates the instability issues. We learn a shared multilingual embedding space for a variable number of languages by incrementally adding new languages one by one to the current multilingual space. Through the gradual language addition the method can leverage the interdependencies between the new language and all other languages in the current multilingual space. We find that it is beneficial to project more distant languages later in the iterative process. Our fully unsupervised multilingual embedding spaces yield results that are on par with the state-of-the-art methods in the bilingual lexicon induction (BLI) task, and simultaneously obtain state-of-the-art scores on two downstream tasks: multilingual document classification and multilingual dependency parsing, outperforming even supervised baselines. This finding also accentuates the need to establish evaluation protocols for cross-lingual word embeddings beyond the omnipresent intrinsic BLI task in future work.
Title: Curriculum Learning for Domain Adaptation in Neural Machine Translation
Venue: NAACL (2019)
Topic: machine translation
Abstract: We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the training algorithm with a particular schedule. This approach is simple to implement on top of any neural framework or architecture, and consistently outperforms both unadapted and adapted baselines in experiments with two distinct domains and two language pairs.
Title: Improving Robustness of Machine Translation with Synthetic Noise
Venue: NAACL (2019)
Topic: machine translation
Abstract: Modern Machine Translation (MT) systems perform remarkably well on clean, in-domain text. However most of the human generated text, particularly in the realm of social media, is full of typos, slang, dialect, idiolect and other noise which can have a disastrous impact on the accuracy of MT. In this paper we propose methods to enhance the robustness of MT systems by emulating naturally occurring noise in otherwise clean data. Synthesizing noise in this manner we are ultimately able to make a vanilla MT system more resilient to naturally occurring noise, partially mitigating loss in accuracy resulting therefrom.
Title: Non-Parametric Adaptation for Neural Machine Translation
Venue: NAACL (2019)
Topic: machine translation
Abstract: Neural Networks trained with gradient descent are known to be susceptible to catastrophic forgetting caused by parameter shift during the training process. In the context of Neural Machine Translation (NMT) this results in poor performance on heterogeneous datasets and on sub-tasks like rare phrase translation. On the other hand, non-parametric approaches are immune to forgetting, perfectly complementing the generalization ability of NMT. However, attempts to combine non-parametric or retrieval based approaches with NMT have only been successful on narrow domains, possibly due to over-reliance on sentence level retrieval. We propose a novel n-gram level retrieval approach that relies on local phrase level similarities, allowing us to retrieve neighbors that are useful for translation even when overall sentence similarity is low. We complement this with an expressive neural network, allowing our model to extract information from the noisy retrieved context. We evaluate our Semi-parametric NMT approach on a heterogeneous dataset composed of WMT, IWSLT, JRC-Acquis and OpenSubtitles, and demonstrate gains on all 4 evaluation sets. The Semi-parametric nature of our approach also opens the door for non-parametric domain adaptation, demonstrating strong inference-time adaptation performance on new domains without the need for any parameter updates.
Title: Online Distilling from Checkpoints for Neural Machine Translation
Venue: NAACL (2019)
Topic: machine translation
Abstract: Current predominant neural machine translation (NMT) models often have a deep structure with large amounts of parameters, making these models hard to train and easily suffering from over-fitting. A common practice is to utilize a validation set to evaluate the training process and select the best checkpoint. Average and ensemble techniques on checkpoints can lead to further performance improvement. However, as these methods do not affect the training process, the system performance is restricted to the checkpoints generated in original training procedure. In contrast, we propose an online knowledge distillation method. Our method on-the-fly generates a teacher model from checkpoints, guiding the training process to obtain better performance. Experiments on several datasets and language pairs show steady improvement over a strong self-attention-based baseline system. We also provide analysis on data-limited setting against over-fitting. Furthermore, our method leads to an improvement in a machine reading experiment as well.
Title: Value-based Search in Execution Space for Mapping Instructions to Programs
Venue: NAACL (2019)
Topic: nlp applications
Abstract: Training models to map natural language instructions to programs, given target world supervision only, requires searching for good programs at training time. Search is commonly done using beam search in the space of partial programs or program trees, but as the length of the instructions grows finding a good program becomes difficult. In this work, we propose a search algorithm that uses the target world state, known at training time, to train a critic network that predicts the expected reward of every search state. We then score search states on the beam by interpolating their expected reward with the likelihood of programs represented by the search state. Moreover, we search not in the space of programs but in a more compressed state of program executions, augmented with recent entities and actions. On the SCONE dataset, we show that our algorithm dramatically improves performance on all three domains compared to standard beam search and other baselines.
Title: VQD: Visual Query Detection In Natural Scenes
Venue: NAACL (2019)
Topic: multimodality and language grounding to vision, robotics and beyond
Abstract: We propose a new visual grounding task called Visual Query Detection (VQD). In VQD, the task is to localize a variable number of objects in an image where the objects are specified in natural language. VQD is related to visual referring expression comprehension, where the task is to localize only one object. We propose the first algorithms for VQD, and we evaluate them on both visual referring expression datasets and our new VQDv1 dataset.