Fingerspelling enables signers to represent proper nouns and technical terms letter-by-letter using manual alphabets, yet remains severely under-resourced for Indian Sign Language (ISL). We present the first continuous fingerspelling dataset for ISL, extracted from the ISH News YouTube channel, in which fingerspelling is accompanied by synchronized on-screen text cues. The dataset comprises 1,308 segments from 499 videos, totaling 70.85 minutes and 14,814 characters, with aligned video-text pairs capturing authentic coarticulation patterns. We validated the dataset quality through annotation using a proficient ISL interpreter, achieving a 90.67% exact match rate for 150 samples. We further established baseline recognition benchmarks using a ByT5-small encoder-decoder model, which attains 82.91% Character Error Rate after fine-tuning. This resource supports multiple downstream tasks, including fingerspelling transcription, temporal localization, and sign generation. The dataset is available at the following link: https://kirandevraj.github.io/ISL-Fingerspelling/.

In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of securing word-level embeddings is very well studied, we propose a novel method for obtaining sentence-level embeddings. This is obtained by a simple method in the context of solving the paraphrase generation task. If we use a sequential encoder-decoder model for generating paraphrase, we would like the generated paraphrase to be semantically close to the original sentence. One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far. This is ensured by using a sequential pair-wise discriminator that shares weights with the encoder that is trained with a suitable loss function. Our loss function penalizes paraphrase sentence embedding distances from being too large. This loss is used in combination with a sequential encoder-decoder network. We also validated our method by evaluating the obtained embeddings for a sentiment analysis task. The proposed method results in semantic embeddings and outperforms the state-of-the-art on the paraphrase generation and sentiment analysis task on standard datasets. These results are also shown to be statistically significant.