We address the challenging task of neural machine translation (NMT) in the entertainment domain, where the objective is to automatically translate a given dialogue from a source language content to a target language. This task has various applications, particularly in automatic dubbing, subtitling, and other content localization tasks, enabling source content to reach a wider audience. Traditional NMT systems typically translate individual sentences in isolation, without facilitating knowledge transfer of crucial elements such as the context and style from previously encountered sentences. In this work, we emphasize the significance of these fundamental aspects in producing pertinent and captivating translations. We demonstrate their significance through several examples and propose a novel framework for entertainment translation, which, to our knowledge, is the first of its kind. Furthermore, we introduce an algorithm to estimate the context and style of the current session and use these estimations to generate a prompt that guides a Large Language Model (LLM) to generate high-quality translations. Our method is both language and LLM-agnostic, making it a general-purpose tool. We demonstrate the effectiveness of our algorithm through various numerical studies and observe significant improvement in the COMET scores over various state-of-the-art LLMs. Moreover, our proposed method consistently outperforms baseline LLMs in terms of win-ratio.

We consider an improper reinforcement learning setting where a learner is given $M$ base controllers for an unknown Markov decision process, and wishes to combine them optimally to produce a potentially new controller that can outperform each of the base ones. This can be useful in tuning across controllers, learnt possibly in mismatched or simulated environments, to obtain a good controller for a given target environment with relatively few trials. Towards this, we propose two algorithms: (1) a Policy Gradient-based approach; and (2) an algorithm that can switch between a simple Actor-Critic (AC) based scheme and a Natural Actor-Critic (NAC) scheme depending on the available information. Both algorithms operate over a class of improper mixtures of the given controllers. For the first case, we derive convergence rate guarantees assuming access to a gradient oracle. For the AC-based approach we provide convergence rate guarantees to a stationary point in the basic AC case and to a global optimum in the NAC case. Numerical results on (i) the standard control theoretic benchmark of stabilizing an inverted pendulum; and (ii) a constrained queueing task show that our improper policy optimization algorithm can stabilize the system even when the base policies at its disposal are unstable.

We study the best arm identification problem in linear multi-armed bandits (LMAB) in the fixed confidence setting ; this is also the problem of optimizing an unknown linear function over a discrete domain with noisy, zeroth-order access. We propose an explicitly implementable and provably order-optimal sample-complexity algorithm for best arm identification. Existing approaches that achieve optimal sample complexity assume either access to a nontrivial minimax optimization oracle (e.g. RAGE and Lazy-TS) or knowledge of an upper-bound on the norm of the unknown parameter vector(e.g. LinGame). Our algorithm, which we call the Phased Elimination Linear Exploration Game (PELEG), maintains a high-probability confidence ellipsoid containing the unknown vector, and uses it to eliminate suboptimal arms in phases, where a minimax problem is essentially solved in each phase using two-player low regret learners. PELEG does not require to know a bound on norm of the unknown vector, and is asymptotically-optimal, settling an open problem. We show that the sample complexity of PELEG matches, up to order and in a non-asymptotic sense, an instance-dependent universal lower bound for linear bandits. PELEG is thus the first algorithm to achieve both order-optimal sample complexity and explicit implementability for this setting. We also provide numerical results for the proposed algorithm consistent with its theoretical guarantees.

We propose a novel application of the acoustic-to-articulatory inversion (AAI) towards a quality assessment of the voice converted speech. The ability of humans to speak effortlessly requires the coordinated movements of various articulators, muscles, etc. This effortless movement contributes towards a naturalness, intelligibility and speaker's identity (which is partially present in voice converted speech). Hence, during voice conversion (VC), the information related to the speech production is lost. In this paper, this loss is quantified for a male voice, by showing an increase in RMSE error (up to 12.7 % in tongue tip) for voice converted speech followed by showing a decrease in mutual information (I) (by 8.7 %). Similar results are obtained in the case of a female voice. This observation is extended by showing that the articulatory features can be used as an objective measure. The effectiveness of the proposed measure over MCD is illustrated by comparing their correlation with a Mean Opinion Score (MOS). Moreover, the preference score of MCD contradicted ABX test by 100 %, whereas the proposed measure supported ABX test by 45.8 % and 16.7 % in the case of female-to-male and male-to-female VC, respectively.