mtybilly/Instruct2DS huggingface dataset:
Uncertain Natural Language Inference
Authors: Zhengping Jiang, Adam Poliak, Keisuke Sakaguchi, Benjamin Van Durme Tongfei Chen
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: We introduce Uncertain Natural Language Inference (UNLI), a refinement of Natural Language Inference (NLI) that shifts away from categorical labels, targeting instead the direct prediction of subjective probability assessments. We demonstrate the feasibility of collecting annotations for UNLI by relabeling a portion of the SNLI dataset under a probabilistic scale, where items even with the same categorical label differ in how likely people judge them to be true given a premise. We describe a direct scalar regression modeling approach, and find that existing categorically-labeled NLI data can be used in pre-training. Our best models correlate well with humans, demonstrating models are capable of more subtle inferences than the categorical bin assignment employed in current NLI tasks.
Extracting Headless MWEs from Dependency Parse Trees: Parsing, Tagging, and Joint Modeling Approaches
Authors: Lillian Lee Tianze Shi
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: An interesting and frequent type of multi-word expression (MWE) is the headless MWE, for which there are no true internal syntactic dominance relations; examples include many named entities (“Wells Fargo”) and dates (“July 5, 2020”) as well as certain productive constructions (“blow for blow”, “day after day”). Despite their special status and prevalence, current dependency-annotation schemes require treating such flat structures as if they had internal syntactic heads, and most current parsers handle them in the same fashion as headed constructions. Meanwhile, outside the context of parsing, taggers are typically used for identifying MWEs, but taggers might benefit from structural information. We empirically compare these two common strategies---parsing and tagging---for predicting flat MWEs. Additionally, we propose an efficient joint decoding algorithm that combines scores from both strategies. Experimental results on the MWE-Aware English Dependency Corpus and on six non-English dependency treebanks with frequent flat structures show that: (1) tagging is more accurate than parsing for identifying flat-structure MWEs, (2) our joint decoder reconciles the two different views and, for non-BERT features, leads to higher accuracies, and (3) most of the gains result from feature sharing between the parsers and taggers.
Revisiting Higher-Order Dependency Parsers
Authors: André F. T. Martins Erick Fonseca
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Neural encoders have allowed dependency parsers to shift from higher-order structured models to simpler first-order ones, making decoding faster and still achieving better accuracy than non-neural parsers. This has led to a belief that neural encoders can implicitly encode structural constraints, such as siblings and grandparents in a tree. We tested this hypothesis and found that neural parsers may benefit from higher-order features, even when employing a powerful pre-trained encoder, such as BERT. While the gains of higher-order features are small in the presence of a powerful encoder, they are consistent for long-range dependencies and long sentences. In particular, higher-order models are more accurate on full sentence parses and on the exact match of modifier lists, indicating that they deal better with larger, more complex structures.
SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling
Authors: Weitong Ruan, Xinyue Liu, Jianhua Lu Luoxin Chen
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: Virtual adversarial training (VAT) is a powerful technique to improve model robustness in both supervised and semi-supervised settings. It is effective and can be easily adopted on lots of image classification and text classification tasks. However, its benefits to sequence labeling tasks such as named entity recognition (NER) have not been shown as significant, mostly, because the previous approach can not combine VAT with the conditional random field (CRF). CRF can significantly boost accuracy for sequence models by putting constraints on label transitions, which makes it an essential component in most state-of-the-art sequence labeling model architectures. In this paper, we propose SeqVAT, a method which naturally applies VAT to sequence labeling models with CRF. Empirical studies show that SeqVAT not only significantly improves the sequence labeling performance over baselines under supervised settings, but also outperforms state-of-the-art approaches under semi-supervised settings.
Treebank Embedding Vectors for Out-of-Domain Dependency Parsing
Authors: James Barry, Jennifer Foster Joachim Wagner
Published in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2020

Abstract: A recent advance in monolingual dependency parsing is the idea of a treebank embedding vector, which allows all treebanks for a particular language to be used as training data while at the same time allowing the model to prefer training data from one treebank over others and to select the preferred treebank at test time. We build on this idea by 1) introducing a method to predict a treebank vector for sentences that do not come from a treebank used in training, and 2) exploring what happens when we move away from predefined treebank embedding vectors during test time and instead devise tailored interpolations. We show that 1) there are interpolated vectors that are superior to the predefined ones, and 2) treebank vectors can be predicted with sufficient accuracy, for nine out of ten test languages, to match the performance of an oracle approach that knows the most suitable predefined treebank embedding for the test set.
Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments
Authors: Nathan Schneider, Brendan O′Connor, Dipanjan Das, Daniel Mills, Jacob Eisenstein, Michael Heilman, Dani Yogatama, Jeffrey Flanigan, Noah A. Smith Kevin Gimpel
Published in: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (Main Conference Track), 2011