1 code implementation • LREC 2022 • Josiah Wang, Pranava Madhyastha, Josiel Figueiredo, Chiraag Lala, Lucia Specia
The dataset will benefit research on visual grounding of words especially in the context of free-form sentences, and can be obtained from https://doi. org/10. 5281/zenodo. 5034604 under a Creative Commons licence.
Ranked #1 on Multimodal Text Prediction on MultiSubs
Multimodal Lexical Translation Multimodal Text Prediction +2
no code implementations • WS 2019 • Chiraag Lala, Pranava Madhyastha, Lucia Specia
Recent work on visually grounded language learning has focused on broader applications of grounded representations, such as visual question answering and multimodal machine translation.
Grounded language learning Multimodal Machine Translation +3
no code implementations • WS 2018 • Chiraag Lala, Pranava Swaroop Madhyastha, Carolina Scarton, Lucia Specia
For task 1b, we explore three approaches: (i) re-ranking based on cross-lingual word sense disambiguation (as for task 1), (ii) re-ranking based on consensus of NMT n-best lists from German-Czech, French-Czech and English-Czech systems, and (iii) data augmentation by generating English source data through machine translation from French to English and from German to English followed by hypothesis selection using a multimodal-reranker.
1 code implementation • WS 2018 • Lo{\"\i}c Barrault, Fethi Bougares, Lucia Specia, Chiraag Lala, Desmond Elliott, Stella Frank
In this task a source sentence in English is supplemented by an image and participating systems are required to generate a translation for such a sentence into German, French or Czech.
no code implementations • 18 Oct 2014 • Chiraag Lala, Shay B. Cohen
We describe a visualization tool that can be used to view the change in meaning of words over time.