Weaknesses: W1 The submission claims that existing approaches only capture spatial appearance (line 42), but the one that is compared with [2] is actually based on RNNs, that have the potential to capture motion information across a sequence of frames. W2 While the work acknowledges the challenges of of motion blurs and fine-grained gesture details (line 40), it does not address them in the proposed approach. W3 The quantitative gains in terms of BLEU (9.58 to 13.41) and ROUGE (31.80 to 34.96) scores are not outstanding. W4 The results of [2] by exploiting the glosses available in the dataset are better than the ones in this submission. Given that the contributions of the work address the visual representation, it is not argues why the proposed techniques are also assess with the Sign-to-Gloss-to-Text set up considered in [2].

The reviewers were positive about the ideas in the paper and mostly debated the merits of the evaluation. For one they were not fully convinced about the arguments in the rebuttal about the differences between the sharpness of boundaries for action localization and sign language translation. For camera ready I would suggest better addressing this point, as well as comparing or justifying differences to "Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation", Camgoz et al, CVPR 2020. One final suggestion is to add results with one more video encoder in addition to I3D.

UPDATE: Authors answered my questions, I would like to keep my score unchanged and suggest to focus on clarity of the final version. Perhaps, this is the case when I would really be interested in looking at the source code. Originality: the paper borrows the general idea of product keys from the database community, however the application to fast retrieval in neural memory systems seems quite novel to me. Quality: The core ideas of the paper are sound, however more I would appreciate more rigor in both conceptual and experimental comparison with other approaches incorporating memory to Transformer (see e.g. Another suggestion would be to discuss more the issue of potential non-uniformity of the query distribution, which indeed seems to be quite relevant.

Large language models (LLMs) are increasingly utilized for machine translation, yet their predictions often exhibit uncertainties that hinder interpretability and user trust. Effectively visualizing these uncertainties can enhance the usability of LLM outputs, particularly in contexts where translation accuracy is critical. This paper addresses two primary objectives: (1) providing users with token-level insights into model confidence and (2) developing a web-based visualization tool to quantify and represent translation uncertainties. To achieve these goals, we utilized the T5 model with the WMT19 dataset for translation tasks and evaluated translation quality using established metrics such as BLEU, METEOR, and ROUGE. We introduced three novel uncertainty quantification (UQ) metrics: (1) the geometric mean of token probabilities, (2) the arithmetic mean of token probabilities, and (3) the arithmetic mean of the kurtosis of token distributions. These metrics provide a simple yet effective framework for evaluating translation performance. Our analysis revealed a linear relationship between the traditional evaluation metrics and our UQ metrics, demonstrating the validity of our approach. Additionally, we developed an interactive web-based visualization that uses a color gradient to represent token confidence. This tool offers users a clear and intuitive understanding of translation quality while providing valuable insights into model performance. Overall, we show that our UQ metrics and visualization are both robust and interpretable, offering practical tools for evaluating and accessing machine translation systems.

This study introduces an approach to Estonian text simplification using two model architectures: a neural machine translation model and a fine-tuned large language model (LLaMA). Given the limited resources for Estonian, we developed a new dataset, the Estonian Simplification Dataset, combining translated data and GPT-4.0-generated simplifications. We benchmarked OpenNMT, a neural machine translation model that frames text simplification as a translation task, and fine-tuned the LLaMA model on our dataset to tailor it specifically for Estonian simplification. Manual evaluations on the test set show that the LLaMA model consistently outperforms OpenNMT in readability, grammaticality, and meaning preservation. These findings underscore the potential of large language models for low-resource languages and provide a basis for further research in Estonian text simplification.

Weaknesses: While the described approach is simple and very generally applicable, there are some major issues with the evaluation that need to be addressed. If 1. and 2. are addressed I would be willing to update my scores. The BLEU evaluation is not clearly described for the WMT and IWSLT experiments. Given the major variations observed in BLEU scores due to differences in post-processing or the BLEU evaluation script used, it's hard to fairly compare against previous work without clearly describing the post-processing, tokenization and BLEU evaluation tool used for these experiments. Since the proposed method relies heavily on using backward and forward translated data, these effects are bound to affect the observed BLEU improvements.

This work describes a simple approach to synthetically augment the training dataset for neural machine translation. The proposed approach involves training multiple forward and backward MT models and appending their outputs on the original training dataset to the training data. This augmented (or diversified) training dataset can then be used to train the next generation of models. The proposed approach is simple, achieves good results, and the authors do a good job presenting the idea. The paper is quite empirical and the technique fairly specific to NMT, but it is still interesting to see that sometimes simple ideas work well and are thus important / deserve careful consideration.

Recent advancements in large language models (LLMs) have demonstrated their remarkable capabilities across various language tasks. Inspired by the success of text-to-text translation refinement, this paper investigates how LLMs can improve the performance of speech translation by introducing a joint refinement process. Through the joint refinement of speech translation (ST) and automatic speech recognition (ASR) transcription via LLMs, the performance of the ST model is significantly improved in both training-free in-context learning and parameter-efficient fine-tuning scenarios. Additionally, we explore the effect of document-level context on refinement under the context-aware fine-tuning scenario. Experimental results on the MuST-C and CoVoST 2 datasets, which include seven translation tasks, demonstrate the effectiveness of the proposed approach using several popular LLMs including GPT-3.5-turbo, LLaMA3-8B, and Mistral-12B. Further analysis further suggests that jointly refining both transcription and translation yields better performance compared to refining translation alone. Meanwhile, incorporating document-level context significantly enhances refinement performance. We release our code and datasets on GitHub.

Ensembling neural machine translation (NMT) models to produce higher-quality translations than the $L$ individual models has been extensively studied. Recent methods typically employ a candidate selection block (CSB) and an encoder-decoder fusion block (FB), requiring inference across \textit{all} candidate models, leading to significant computational overhead, generally $\Omega(L)$. This paper introduces \textbf{SmartGen}, a reinforcement learning (RL)-based strategy that improves the CSB by selecting a small, fixed number of candidates and identifying optimal groups to pass to the fusion block for each input sentence. Furthermore, previously, the CSB and FB were trained independently, leading to suboptimal NMT performance. Our DQN-based \textbf{SmartGen} addresses this by using feedback from the FB block as a reward during training. We also resolve a key issue in earlier methods, where candidates were passed to the FB without modification, by introducing a Competitive Correction Block (CCB). Finally, we validate our approach with extensive experiments on English-Hindi translation tasks in both directions.

The paper proposes to boost translation quality of a non-autoregressive (NART) neural machine translation system through a conditional random field (CRF) that is attached to the decoder. The CRF reduces the translation quality drop compared to autoregressive neural translation systems by imposing a bigram-language model like structure onto the decoder that helps to alleviate the strong independence assumption that NART architectures entail. The CRF is jointly trained with all other parameters of the neural network. Experiments conducted on WMT14 and IWSLT14 En-De and De-En tasks are reported to yield improvements of more than 6 BLEU points over their corresponding baselines. By augmenting the decoder with a Markov-order 1 CRF, the resulting network is strictly speaking no longer a non-autoregressive system.