activity
20142023
most citedTailoring Word Embeddings for Bilexical Predictions: An Experimental Comparison

3 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2023

Towards Robust Aspect-based Sentiment Analysis through Non-counterfactual Augmentations

Xinyu Liu, Yan Ding, Kaikai An +4

While state-of-the-art NLP models have demonstrated excellent performance for aspect based sentiment analysis (ABSA), substantial evidence has been presented on their lack of robus…

cs.CL20231 cited

Are words equally surprising in audio and audio-visual comprehension?

Pranava Madhyastha, Ye Zhang, Gabriella Vigliocco

We report a controlled study investigating the effect of visual information (i.e., seeing the speaker) on spoken language comprehension. We compare the ERP signature (N400) associa…

cs.CL2023

Towards preserving word order importance through Forced Invalidation

Hadeel Al-Negheimish, Pranava Madhyastha, Alessandra Russo

Large pre-trained language models such as BERT have been widely used as a framework for natural language understanding (NLU) tasks. However, recent findings have revealed that pre-…

cs.LG2023

Theoretical Conditions and Empirical Failure of Bracket Counting on Long Sequences with Linear Recurrent Networks

Nadine El-Naggar, Pranava Madhyastha, Tillman Weyde

Previous work has established that RNNs with an unbounded activation function have the capacity to count exactly. However, it has also been shown that RNNs are challenging to train…

cs.CL20231 cited

Towards a Unified Model for Generating Answers and Explanations in Visual Question Answering

Chenxi Whitehouse, Tillman Weyde, Pranava Madhyastha

The field of visual question answering (VQA) has recently seen a surge in research focused on providing explanations for predicted answers. However, current systems mostly rely on…

cs.CL20161 cited

Resolving Out-of-Vocabulary Words with Bilingual Embeddings in Machine Translation

Pranava Swaroop Madhyastha, Cristina España-Bonet

Out-of-vocabulary words account for a large proportion of errors in machine translation systems, especially when the system is used on a different domain than the one where it was…