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Found 25 papers, 8 papers with code
When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection
Learning fine-grained distinctions between vocabulary items is a key challenge in learning a new language.
It's All Relative! -- A Synthetic Query Generation Approach for Improving Zero-Shot Relevance Prediction
Recent developments in large language models (LLMs) have shown promise in their ability to generate synthetic query-document pairs by prompting with as few as 8 demonstrations.
Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learning
This is challenging because i) it requires that such experts be accessible and have the necessary resources, and ii) describing all the intricacies of a language is time-consuming and prone to omission.
Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting
Idioms are common in everyday language, but often pose a challenge to translators because their meanings do not follow from the meanings of their parts.
Ambiguity-Aware In-Context Learning with Large Language Models
In-context learning (ICL) i. e. showing LLMs only a few task-specific demonstrations has led to downstream gains with no task-specific fine-tuning required.
Exploring the Viability of Synthetic Query Generation for Relevance Prediction
While our experiments demonstrate that these modifications help improve performance of QGen techniques, we also find that QGen approaches struggle to capture the full nuance of the relevance label space and as a result the generated queries are not faithful to the desired relevance label.
Salient Span Masking for Temporal Understanding
First, we find that SSM alone improves the downstream performance on three temporal tasks by an avg.
Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learning
This process is challenging because i) it requires that such experts be accessible and have the necessary resources, and ii) even if there are such experts, describing all the intricacies of a language is time-consuming and prone to omission.
AUTOLEX: An Automatic Framework for Linguistic Exploration
Each language has its own complex systems of word, phrase, and sentence construction, the guiding principles of which are often summarized in grammar descriptions for the consumption of linguists or language learners.
When is Wall a Pared and when a Muro? -- Extracting Rules Governing Lexical Selection
Learning fine-grained distinctions between vocabulary items is a key challenge in learning a new language.
Do Context-Aware Translation Models Pay the Right Attention?
Are models paying large amounts of attention to the same context?
Evaluating the Morphosyntactic Well-formedness of Generated Texts
Text generation systems are ubiquitous in natural language processing applications.
Reducing Confusion in Active Learning for Part-Of-Speech Tagging
Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost.
DICT-MLM: Improved Multilingual Pre-Training using Bilingual Dictionaries
In particular, by requiring the model to predict the language-specific token, the MLM objective disincentivizes learning a language-agnostic representation -- which is a key goal of multilingual pre-training.
SIGTYP 2020 Shared Task: Prediction of Typological Features
Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world's languages.
Automatic Extraction of Rules Governing Morphological Agreement
Using cross-lingual transfer, even with no expert annotations in the language of interest, our framework extracts a grammatical specification which is nearly equivalent to those created with large amounts of gold-standard annotated data.
Exploring Neural Architectures And Techniques For Typologically Diverse Morphological Inflection
Morphological inflection in low resource languages is critical to augment existing corpora in Low Resource Languages, which can help develop several applications in these languages with very good social impact.
A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization
Despite recent advances in natural language processing and other language technology, the application of such technology to language documentation and conservation has been limited.
What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection
Irony detection is an important task with applications in identification of online abuse and harassment.
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers
Most state-of-the-art models for named entity recognition (NER) rely on the availability of large amounts of labeled data, making them challenging to extend to new, lower-resourced languages.
Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations
This paper presents the submissions by Team Dr. Quad to the ACL-BioNLP 2019 shared task on Textual Inference and Question Entailment in the Medical Domain.
CMU-01 at the SIGMORPHON 2019 Shared Task on Crosslinguality and Context in Morphology
This paper presents the submission by the CMU-01 team to the SIGMORPHON 2019 task 2 of Morphological Analysis and Lemmatization in Context.
The ARIEL-CMU Systems for LoReHLT18
This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).
Weighted Global Normalization for Multiple Choice Reading Comprehension over Long Documents
Motivated by recent evidence pointing out the fragility of high-performing span prediction models, we direct our attention to multiple choice reading comprehension.
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations
Much work in Natural Language Processing (NLP) has been for resource-rich languages, making generalization to new, less-resourced languages challenging.
