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Found 20 papers, 7 papers with code
CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation
Cultural content poses challenges for machine translation systems due to the differences in conceptualizations between cultures, where language alone may fail to convey sufficient context to capture region-specific meanings.
IteRABRe: Iterative Recovery-Aided Block Reduction
Large Language Models (LLMs) have grown increasingly expensive to deploy, driving the need for effective model compression techniques.
Pralekha: An Indic Document Alignment Evaluation Benchmark
To address the challenge of aligning documents using sentence and chunk-level alignments, we propose a novel scoring method, Document Alignment Coefficient (DAC).
Connecting Ideas in 'Lower-Resource' Scenarios: NLP for National Varieties, Creoles and Other Low-resource Scenarios
Despite excellent results on benchmarks over a small subset of languages, large language models struggle to process text from languages situated in `lower-resource' scenarios such as dialects/sociolects (national or social varieties of a language), Creoles (languages arising from linguistic contact between multiple languages) and other low-resource languages.
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge present in both visual and textual data.
Enhancing Personality Recognition in Dialogue by Data Augmentation and Heterogeneous Conversational Graph Networks
Personality recognition is useful for enhancing robots' ability to tailor user-adaptive responses, thus fostering rich human-robot interactions.
Bilingual Corpus Mining and Multistage Fine-Tuning for Improving Machine Translation of Lecture Transcripts
To create the parallel corpora, we propose a dynamic programming based sentence alignment algorithm which leverages the cosine similarity of machine-translated sentences.
Variable-length Neural Interlingua Representations for Zero-shot Neural Machine Translation
The language-independency of encoded representations within multilingual neural machine translation (MNMT) models is crucial for their generalization ability on zero-shot translation.
Exploring the Impact of Layer Normalization for Zero-shot Neural Machine Translation
This paper studies the impact of layer normalization (LayerNorm) on zero-shot translation (ZST).
GPT-RE: In-context Learning for Relation Extraction using Large Language Models
In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e. g., GPT-3), they still lag significantly behind fully-supervised baselines (e. g., fine-tuned BERT) in relation extraction (RE).
Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision
Contrastive pre-training on distant supervision has shown remarkable effectiveness in improving supervised relation extraction tasks.
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?
Meanwhile, the contrastive objective can implicitly utilize automatically learned word alignment, which has not been explored in many-to-many NMT.
Video-guided Machine Translation with Spatial Hierarchical Attention Network
Video-guided machine translation, as one type of multimodal machine translations, aims to engage video contents as auxiliary information to address the word sense ambiguity problem in machine translation.
A System for Worldwide COVID-19 Information Aggregation
The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education.
Pre-training via Leveraging Assisting Languages for Neural Machine Translation
Sequence-to-sequence (S2S) pre-training using large monolingual data is known to improve performance for various S2S NLP tasks.
JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation
Monolingual pre-training approaches such as MASS (MAsked Sequence to Sequence) are extremely effective in boosting NMT quality for languages with small parallel corpora.
Pre-training via Leveraging Assisting Languages and Data Selection for Neural Machine Translation
To this end, we propose to exploit monolingual corpora of other languages to complement the scarcity of monolingual corpora for the LOI.
Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation
To address this, we examine a language independent framework for parallel corpus mining which is a quick and effective way to mine a parallel corpus from publicly available lectures at Coursera.
Invocation-driven Neural Approximate Computing with a Multiclass-Classifier and Multiple Approximators
We thus propose a novel approximate computing architecture with a Multiclass-Classifier and Multiple Approximators (MCMA).
