Person
Prakhar Gupta
This person is no longer with EPFL
Related publications (7)
Natural Language Processing (NLP) driven categorisation and detection of discourse in historical US patents
Jérôme Baudry, Nicolas Christophe Chachereau, Bhargav Srinivasa Desikan, Prakhar Gupta
Patents have traditionally been used in the history of technology as an indication of the thinking process of the inventors, of the challenges or “reverse salients” they faced, or of the social groups influencing the construction of technology. More recent ...
2022Learning computationally efficient static word and sentence representations
Most of the Natural Language Processing (NLP) algorithms involve use of distributed vector representations of linguistic units (primarily words and sentences) also known as embeddings in one way or another. These embeddings come in two flavours namely, sta ...
EPFL2021Lightweight Cross-Lingual Sentence Representation Learning
Martin Jaggi, Prakhar Gupta, Zhuoyuan Mao
Large-scale models for learning fixed-dimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks. However, further increases and modifications based on such ...
ASSOC COMPUTATIONAL LINGUISTICS-ACL2021Design Patterns for Resource-Constrained Automated Deep-Learning Methods
We present an extensive evaluation of a wide variety of promising design patterns for automated deep-learning (AutoDL) methods, organized according to the problem categories of the 2019 AutoDL challenges, which set the task of optimizing both model accura ...
2020Better Word Embeddings by Disentangling Contextual n-Gram Information
Martin Jaggi, Matteo Pagliardini, Prakhar Gupta
Pre-trained word vectors are ubiquitous in Natural Language Processing applications. In this paper, we show how training word embeddings jointly with bigram and even trigram embeddings, results in improved unigram embeddings. We claim that training word em ...
2019Learning Word Vectors for 157 Languages
Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train them on ...
2018Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
Martin Jaggi, Matteo Pagliardini, Prakhar Gupta
The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simpl ...
2017