To address this challenge, we propose a novel data augmentation method FlipDA that jointly uses a generative model and a classifier to generate label-flipped data. We propose that n-grams composed of random character sequences, or garble, provide a novel context for studying word meaning both within and beyond extant language. We also propose a general Multimodal Dialogue-aware Interaction framework, MDI, to model the dialogue context for emotion recognition, which achieves comparable performance to the state-of-the-art methods on the M 3 ED. The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Fail. Probing Structured Pruning on Multilingual Pre-trained Models: Settings, Algorithms, and Efficiency. Specifically, we devise a three-stage training framework to incorporate the large-scale in-domain chat translation data into training by adding a second pre-training stage between the original pre-training and fine-tuning stages. In an educated manner. These results have prompted researchers to investigate the inner workings of modern PLMs with the aim of understanding how, where, and to what extent they encode information about SRL. Getting a tough clue should result in a definitive "Ah, OK, right, yes. " We present a framework for learning hierarchical policies from demonstrations, using sparse natural language annotations to guide the discovery of reusable skills for autonomous decision-making. Despite promising recentresults, we find evidence that reference-freeevaluation metrics of summarization and dialoggeneration may be relying on spuriouscorrelations with measures such as word overlap, perplexity, and length. We observe that FaiRR is robust to novel language perturbations, and is faster at inference than previous works on existing reasoning datasets. Especially, even without an external language model, our proposed model raises the state-of-the-art performances on the widely accepted Lip Reading Sentences 2 (LRS2) dataset by a large margin, with a relative improvement of 30%.
Promising experimental results are reported to show the values and challenges of our proposed tasks, and motivate future research on argument mining. Idioms are unlike most phrases in two important ways. We focus on the task of creating counterfactuals for question answering, which presents unique challenges related to world knowledge, semantic diversity, and answerability. Archival runs of 26 of the most influential, longest-running serial publications covering LGBT interests. Moreover, we create a large-scale cross-lingual phrase retrieval dataset, which contains 65K bilingual phrase pairs and 4. In an educated manner wsj crossword solver. To overcome this limitation, we enrich the natural, gender-sensitive MuST-SHE corpus (Bentivogli et al., 2020) with two new linguistic annotation layers (POS and agreement chains), and explore to what extent different lexical categories and agreement phenomena are impacted by gender skews. Data augmentation with RGF counterfactuals improves performance on out-of-domain and challenging evaluation sets over and above existing methods, in both the reading comprehension and open-domain QA settings. We conduct experiments on two text classification datasets – Jigsaw Toxicity, and Bias in Bios, and evaluate the correlations between metrics and manual annotations on whether the model produced a fair outcome.
With a base PEGASUS, we push ROUGE scores by 5. The detection of malevolent dialogue responses is attracting growing interest. Experiments on benchmarks show that the pretraining approach achieves performance gains of up to 6% absolute F1 points. We benchmark several state-of-the-art OIE systems using BenchIE and demonstrate that these systems are significantly less effective than indicated by existing OIE benchmarks. In an educated manner crossword clue. To overcome the problems, we present a novel knowledge distillation framework that gathers intermediate representations from multiple semantic granularities (e. g., tokens, spans and samples) and forms the knowledge as more sophisticated structural relations specified as the pair-wise interactions and the triplet-wise geometric angles based on multi-granularity representations.
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension. We compare uncertainty sampling strategies and their advantages through thorough error analysis. SemAE uses dictionary learning to implicitly capture semantic information from the review text and learns a latent representation of each sentence over semantic units. WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types.
The model utilizes mask attention matrices with prefix adapters to control the behavior of the model and leverages cross-modal contents like AST and code comment to enhance code representation. Despite the surge of new interpretation methods, it remains an open problem how to define and quantitatively measure the faithfulness of interpretations, i. e., to what extent interpretations reflect the reasoning process by a model. Lexical ambiguity poses one of the greatest challenges in the field of Machine Translation. This effectively alleviates overfitting issues originating from training domains. Second, the extraction is entirely data-driven, and there is no need to explicitly define the schemas. To ensure better fusion of examples in multilingual settings, we propose several techniques to improve example interpolation across dissimilar languages under heavy data imbalance. Results on code-switching sets demonstrate the capability of our approach to improve model generalization to out-of-distribution multilingual examples. We delineate key challenges for automated learning from explanations, addressing which can lead to progress on CLUES in the future.
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER. This holistic vision can be of great interest for future works in all the communities concerned by this debate. Alternative Input Signals Ease Transfer in Multilingual Machine Translation. We ask the question: is it possible to combine complementary meaning representations to scale a goal-directed NLG system without losing expressiveness? In this paper, we propose GLAT, which employs the discrete latent variables to capture word categorical information and invoke an advanced curriculum learning technique, alleviating the multi-modality problem. In this work, we investigate Chinese OEI with extremely-noisy crowdsourcing annotations, constructing a dataset at a very low cost. Further analysis also shows that our model can estimate probabilities of candidate summaries that are more correlated with their level of quality. High society held no interest for them.
We release our training material, annotation toolkit and dataset at Transkimmer: Transformer Learns to Layer-wise Skim. It aims to pull close positive examples to enhance the alignment while push apart irrelevant negatives for the uniformity of the whole representation ever, previous works mostly adopt in-batch negatives or sample from training data at random. The experimental results show that, with the enhanced marker feature, our model advances baselines on six NER benchmarks, and obtains a 4. In lexicalist linguistic theories, argument structure is assumed to be predictable from the meaning of verbs. To achieve this, our approach encodes small text chunks into independent representations, which are then materialized to approximate the shallow representation of BERT. QuoteR: A Benchmark of Quote Recommendation for Writing. This hierarchy of codes is learned through end-to-end training, and represents fine-to-coarse grained information about the input. King Charles's sister crossword clue. Plains Cree (nêhiyawêwin) is an Indigenous language that is spoken in Canada and the USA. Empirical results show that our framework outperforms prior methods substantially and it is more robust to adversarially annotated examples with our constrained decoding design.
Results show that our simple method gives better results than the self-attentive parser on both PTB and CTB. By formulating EAE as a language generation task, our method effectively encodes event structures and captures the dependencies between arguments. To investigate this question, we apply mT5 on a language with a wide variety of dialects–Arabic. The methodology has the potential to contribute to the study of open questions such as the relative chronology of sound shifts and their geographical distribution.
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