Such slang, in which a set phrase is used instead of the more standard expression with which it rhymes, as in "elephant's trunk" instead of "drunk" (, 94), has in London even "spread from the working-class East End to well-educated dwellers in suburbia, who practise it to exercise their brains just as they might eagerly try crossword puzzles" (, 97). Language Correspondences | Language and Communication: Essential Concepts for User Interface and Documentation Design | Oxford Academic. Boardroom accessories. Pre-trained sequence-to-sequence models have significantly improved Neural Machine Translation (NMT). We provide extensive experiments establishing advantages of pyramid BERT over several baselines and existing works on the GLUE benchmarks and Long Range Arena (CITATION) datasets. We question the validity of the current evaluation of robustness of PrLMs based on these non-natural adversarial samples and propose an anomaly detector to evaluate the robustness of PrLMs with more natural adversarial samples.
It is an extremely low resource language, with no existing corpus that is both available and prepared for supporting the development of language technologies. The ability to recognize analogies is fundamental to human cognition. Our results show that strategic fine-tuning using datasets from other high-resource dialects is beneficial for a low-resource dialect. However, in low resource settings, validation-based stopping can be risky because a small validation set may not be sufficiently representative, and the reduction in the number of samples by validation split may result in insufficient samples for training. Existing solutions, however, either ignore external unstructured data completely or devise dataset-specific solutions. While deep reinforcement learning has shown effectiveness in developing the game playing agent, the low sample efficiency and the large action space remain to be the two major challenges that hinder the DRL from being applied in the real world. These questions often involve three time-related challenges that previous work fail to adequately address: 1) questions often do not specify exact timestamps of interest (e. g., "Obama" instead of 2000); 2) subtle lexical differences in time relations (e. g., "before" vs "after"); 3) off-the-shelf temporal KG embeddings that previous work builds on ignore the temporal order of timestamps, which is crucial for answering temporal-order related questions. Linguistic term for a misleading cognate crossword puzzles. Other possible auxiliary tasks to improve the learning performance have not been fully investigated. There is mounting evidence that existing neural network models, in particular the very popular sequence-to-sequence architecture, struggle to systematically generalize to unseen compositions of seen components. Even though several methods have proposed to defend textual neural network (NN) models against black-box adversarial attacks, they often defend against a specific text perturbation strategy and/or require re-training the models from scratch. We demonstrate the effectiveness of these perturbations in multiple applications. Event extraction is typically modeled as a multi-class classification problem where event types and argument roles are treated as atomic symbols. 1 F1-scores on 10-shot setting) and achieves new state-of-the-art performance. Automatic Identification and Classification of Bragging in Social Media.
Word-level Perturbation Considering Word Length and Compositional Subwords. Extensive experiments show that tuning pre-trained prompts for downstream tasks can reach or even outperform full-model fine-tuning under both full-data and few-shot settings. Maryam Fazel-Zarandi. In experiments with expert and non-expert users and commercial / research models for 8 different tasks, AdaTest makes users 5-10x more effective at finding bugs than current approaches, and helps users effectively fix bugs without adding new bugs. In this paper, we show that it is possible to directly train a second-stage model performing re-ranking on a set of summary candidates. Existing approaches typically rely on a large amount of labeled utterances and employ pseudo-labeling methods for representation learning and clustering, which are label-intensive, inefficient, and inaccurate. Examples of false cognates in english. Our experimental results show that even in cases where no biases are found at word-level, there still exist worrying levels of social biases at sense-level, which are often ignored by the word-level bias evaluation measures. We also introduce new metrics for capturing rare events in temporal windows. Local models for Entity Disambiguation (ED) have today become extremely powerful, in most part thanks to the advent of large pre-trained language models. Hildesheim: Gerstenberg. Lastly, we present a comparative study on the types of knowledge encoded by our system showing that causal and intentional relationships benefit the generation task more than other types of commonsense relations.
Our method does not require task-specific supervision for knowledge integration, or access to a structured knowledge base, yet it improves performance of large-scale, state-of-the-art models on four commonsense reasoning tasks, achieving state-of-the-art results on numerical commonsense (NumerSense), general commonsense (CommonsenseQA 2. To address this problem, we devise DiCoS-DST to dynamically select the relevant dialogue contents corresponding to each slot for state updating. They have been shown to perform strongly on subject-verb number agreement in a wide array of settings, suggesting that they learned to track syntactic dependencies during their training even without explicit supervision. CSC is challenging since many Chinese characters are visually or phonologically similar but with quite different semantic meanings. After reviewing the language's history, linguistic features, and existing resources, we (in collaboration with Cherokee community members) arrive at a few meaningful ways NLP practitioners can collaborate with community partners. We further propose a resource-efficient and modular domain specialization by means of domain adapters – additional parameter-light layers in which we encode the domain knowledge. Linguistic term for a misleading cognate crossword solver. We evaluate our method on four common benchmark datasets including Laptop14, Rest14, Rest15, Rest16. The experiments on two large-scaled news corpora demonstrate that the proposed model can achieve competitive performance with many state-of-the-art alternatives and illustrate its appropriateness from an explainability perspective.
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation. To this end, we train a bi-encoder QA model, which independently encodes passages and questions, to match the predictions of a more accurate cross-encoder model on 80 million synthesized QA pairs. Exaggerate intonation and stress. Using Cognates to Develop Comprehension in English. Unfortunately, existing prompt engineering methods require significant amounts of labeled data, access to model parameters, or both. During that time, many people left the area because of persistent and sustained winds which disrupted their topsoil and consequently the desirability of their land.
Empirical results on three machine translation tasks demonstrate that the proposed model, against the vanilla one, achieves competitable accuracy while saving 99% and 66% energy during alignment calculation and the whole attention procedure. In addition, we introduce a novel controlled Transformer-based decoder to guarantee that key entities appear in the questions. Exam for HS studentsPSAT. Using NLP to quantify the environmental cost and diversity benefits of in-person NLP conferences. Experimental results show the proposed method achieves state-of-the-art performance on a number of measures. CWI is highly dependent on context, whereas its difficulty is augmented by the scarcity of available datasets which vary greatly in terms of domains and languages. We develop a simple but effective "token dropping" method to accelerate the pretraining of transformer models, such as BERT, without degrading its performance on downstream tasks. 'Et __' (and others)ALIA.
We find that giving these models human-written summaries instead of the original text results in a significant increase in acceptability of generated questions (33% → 83%) as determined by expert annotators. Thus, we recommend that future selective prediction approaches should be evaluated across tasks and settings for reliable estimation of their capabilities. The rule and fact selection steps select the candidate rule and facts to be used and then the knowledge composition combines them to generate new inferences. We focus on two kinds of improvements: 1) improving the QA system's performance itself, and 2) providing the model with the ability to explain the correctness or incorrectness of an collect a retrieval-based QA dataset, FeedbackQA, which contains interactive feedback from users. Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages.
FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation. It remains an open question whether incorporating external knowledge benefits commonsense reasoning while maintaining the flexibility of pretrained sequence models. Ferguson explains that speakers of a language containing both "high" and "low" varieties may even deny the existence of the low variety (, 329-30). We propose to tackle this problem by generating a debiased version of a dataset, which can then be used to train a debiased, off-the-shelf model, by simply replacing its training data. We craft a set of operations to modify the control codes, which in turn steer generation towards targeted attributes.