Sonja Schmer-Galunder. On the commonly-used SGD and Weather benchmarks, the proposed self-training approach improves tree accuracy by 46%+ and reduces the slot error rates by 73%+ over the strong T5 baselines in few-shot settings. Continual Pre-training of Language Models for Math Problem Understanding with Syntax-Aware Memory Network.
The dataset provides fine-grained annotation of aligned spans between proverbs and narratives, and contains minimal lexical overlaps between narratives and proverbs, ensuring that models need to go beyond surface-level reasoning to succeed. W. Gunther Plaut, xxix-xxxvi. Adapting Coreference Resolution Models through Active Learning. Novelist DeightonLEN. We conduct experiments on six languages and two cross-lingual NLP tasks (textual entailment, sentence retrieval). For example, one Hebrew scholar explains: "But modern scholarship has come more and more to the conclusion that beneath the legendary embellishments there is a solid core of historical memory, that Abraham and Moses really lived, and that the Egyptian bondage and the Exodus are undoubted facts" (, xxxv). The models, the code, and the data can be found in Controllable Dictionary Example Generation: Generating Example Sentences for Specific Targeted Audiences. Hyperbolic neural networks have shown great potential for modeling complex data. In particular, our method surpasses the prior state-of-the-art by a large margin on the GrailQA leaderboard. Examples of false cognates in english. This work presents a simple yet effective strategy to improve cross-lingual transfer between closely related varieties. Print-ISBN-13: 978-83-226-3752-4.
This inclusive approach results in datasets more representative of actually occurring online speech and is likely to facilitate the removal of the social media content that marginalized communities view as causing the most harm. By exploring a set of feature attribution methods that assign relevance scores to the inputs to explain model predictions, we study the behaviour of state-of-the-art sentence-level QE models and show that explanations (i. rationales) extracted from these models can indeed be used to detect translation errors. Specifically, it first retrieves turn-level utterances of dialogue history and evaluates their relevance to the slot from a combination of three perspectives: (1) its explicit connection to the slot name; (2) its relevance to the current turn dialogue; (3) Implicit Mention Oriented Reasoning. Both enhancements are based on pre-trained language models. We ask the question: is it possible to combine complementary meaning representations to scale a goal-directed NLG system without losing expressiveness? Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages. Our code is available at Knowledge Graph Embedding by Adaptive Limit Scoring Loss Using Dynamic Weighting Strategy. Despite recent progress in abstractive summarization, systems still suffer from faithfulness errors. Language Correspondences | Language and Communication: Essential Concepts for User Interface and Documentation Design | Oxford Academic. I will not attempt to reconcile this larger textual issue, but will limit my attention to a consideration of the Babel account itself. A direct link is made between a particular language element—a word or phrase—and the language used to express its meaning, which stands in or substitutes for that element in a variety of ways. At present, Russian medical NLP is lacking in both datasets and trained models, and we view this work as an important step towards filling this gap. Furthermore, we propose to utilize multi-modal contents to learn representation of code fragment with contrastive learning, and then align representations among programming languages using a cross-modal generation task. Learned self-attention functions in state-of-the-art NLP models often correlate with human attention. Our method provides strong results on multiple experimental settings, proving itself to be both expressive and versatile.
To this end, we first propose a novel task—Continuously-updated QA (CuQA)—in which multiple large-scale updates are made to LMs, and the performance is measured with respect to the success in adding and updating knowledge while retaining existing knowledge. Previous neural approaches for unsupervised Chinese Word Segmentation (CWS) only exploits shallow semantic information, which can miss important context. 92 F1) and strong performance on CTB (92. We release the difficulty scores and hope our work will encourage research in this important yet understudied field of leveraging instance difficulty in evaluations. Besides, we modify the gradients of auxiliary tasks based on their gradient conflicts with the main task, which further boosts the model performance. Improving Controllable Text Generation with Position-Aware Weighted Decoding. Using Cognates to Develop Comprehension in English. In this paper, we imitate the human reading process in connecting the anaphoric expressions and explicitly leverage the coreference information of the entities to enhance the word embeddings from the pre-trained language model, in order to highlight the coreference mentions of the entities that must be identified for coreference-intensive question answering in QUOREF, a relatively new dataset that is specifically designed to evaluate the coreference-related performance of a model. ILL. Oscar nomination, in headlines. Experimental results on eight languages have shown that LiLT can achieve competitive or even superior performance on diverse widely-used downstream benchmarks, which enables language-independent benefit from the pre-training of document layout structure.
Suffix for luncheonETTE. Explaining Classes through Stable Word Attributions. Different from existing works, our approach does not require a huge amount of randomly collected datasets. Considering large amounts of spreadsheets available on the web, we propose FORTAP, the first exploration to leverage spreadsheet formulas for table pretraining.
A more useful text generator should leverage both the input text and the control signal to guide the generation, which can only be built with deep understanding of the domain knowledge. Our code and datasets will be made publicly available. In Tales of the North American Indians, selected and annotated by Stith Thompson, 263. However, the majority of existing methods with vanilla encoder-decoder structures fail to sufficiently explore all of them. What is an example of cognate. Machine Reading Comprehension (MRC) reveals the ability to understand a given text passage and answer questions based on it. 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.