arXiv subjects

Harshit Dhankhar

Publications and source records attributed to Harshit Dhankhar.

2 recordsLinked to original sources

arXiv2026

Balancing Global Quality and Pronoun-Specific Feedback for Context-Aware Machine Translation

Context-aware machine translation can expose the evidence needed for pronoun choice, but standard fine-tuning does not explicitly prioritize these sparse discourse-sensitive decisions. We study ProNMT, a reward-guided iterative self-training method that combines sentence-level quality estimation with a signed confidence signal at generated pronoun positions. For each current sentence and its preceding source context, ProNMT samples candidate translations, scores them using reference-free quality estimation together with a reference-derived pronoun label, and fine-tunes on the highest-scoring candidate. On filtered English--German Europarl and English--French News Commentary data, ProNMT improves over context-aware supervised fine-tuning on BLEU and COMET. Ablations show that pronoun-only feedback can severely degrade sentence-level translation quality on these pronoun-focused data, while hard binary feedback underperforms confidence-weighted feedback. These results indicate that targeted linguistic feedback is most useful when combined with both a global quality signal and the context relevant to the targeted decision. We make the code publicly available at https://github.com/Harshit2807161/ProNMT.

cs.CL↗

arXiv2025

Tabular and Deep Reinforcement Learning for Gittins Index

In the realm of multi-arm bandit problems, the Gittins index policy is known to be optimal in maximizing the expected total discounted reward obtained from pulling the Markovian arms. In most realistic scenarios however, the Markovian state transition probabilities are unknown and therefore the Gittins indices cannot be computed. One can then resort to reinforcement learning (RL) algorithms that explore the state space to learn these indices while exploiting to maximize the reward collected. In this work, we propose tabular (QGI) and Deep RL (DGN) algorithms for learning the Gittins index that are based on the retirement formulation for the multi-arm bandit problem. When compared with existing RL algorithms that learn the Gittins index, our algorithms have a lower run time, require less storage space (small Q-table size in QGI and smaller replay buffer in DGN), and illustrate better empirical convergence to the Gittins index. This makes our algorithm well suited for problems with large state spaces and is a viable alternative to existing methods. As a key application, we demonstrate the use of our algorithms in minimizing the mean flowtime in a job scheduling problem when jobs are available in batches and have an unknown service time distribution.

cs.LG↗