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20182026
most citedComposition-based Multi-Relational Graph Convolutional Networks

136 citations · 387 across the 13 of their papers we have counts for

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13 papers · 1 filter

cs.CL202549 cited

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…

cs.CL2024

STAB: Speech Tokenizer Assessment Benchmark

Shikhar Vashishth, Harman Singh, Shikhar Bharadwaj +6

Representing speech as discrete tokens provides a framework for transforming speech into a format that closely resembles text, thus enabling the use of speech as an input to the wi…

cs.CL2024

A Morphology-Based Investigation of Positional Encodings

Poulami Ghosh, Shikhar Vashishth, Raj Dabre +1

Contemporary deep learning models effectively handle languages with diverse morphology despite not being directly integrated into them. Morphology and word order are closely linked…

cs.CL20232 cited

Self-Influence Guided Data Reweighting for Language Model Pre-training

Megh Thakkar, Tolga Bolukbasi, Sriram Ganapathy +3

Language Models (LMs) pre-trained with self-supervision on large text corpora have become the default starting point for developing models for various NLP tasks. Once the pre-train…

cs.CL2023

Multimodal Modeling For Spoken Language Identification

Shikhar Bharadwaj, Min Ma, Shikhar Vashishth +10

Spoken language identification refers to the task of automatically predicting the spoken language in a given utterance. Conventionally, it is modeled as a speech-based language ide…

cs.CL2023

Label Aware Speech Representation Learning For Language Identification

Shikhar Vashishth, Shikhar Bharadwaj, Sriram Ganapathy +5

Speech representation learning approaches for non-semantic tasks such as language recognition have either explored supervised embedding extraction methods using a classifier model…