With the rapid development of Natural Language Processing (NLP) technology, the accuracy and efficiency of machine translation have become hot topics of research. This paper proposes a novel Seq2Seq model aimed at improving translation quality while reducing the storage space required by the model. The model employs a Bidirectional Long Short-Term Memory network (Bi-LSTM) as the encoder to capture the context information of the input sequence; the decoder incorporates an attention mechanism, enhancing the model’s ability to focus on key information during the translation process. Compared to the current mainstream Transformer model, our model achieves superior performance on the WMT14 machine translation dataset while maintaining a smaller size.
The study first introduces the design principles and innovative points of the model architecture, followed by a series of experiments to verify the effectiveness of the model. The experimental includes an assessment of the model’s performance on different language pairs, as well as comparative analysis with traditional Seq2Seq models. The results show that while maintaining translation accuracy, our model significantly reduces the storage requirements, which is of great significance for translation applications in resource-constrained scenarios.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2410.22335 [cs.CL] |
| (or arXiv:2410.22335v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2410.22335Focus to learn more |
Submission history
From: Yuxu Wu [view email]
[v1] Tue, 29 Oct 2024 01:12:50 UTC (70 KB)
[v2] Thu, 31 Oct 2024 02:32:24 UTC (144 KB)
Here is an evaluation of the provided abstract about the proposed Seq2Seq model using Bi-LSTM and attention for machine translation:
Strengths:
- Relevant Problem and Contribution:
The abstract tackles an important challenge: improving translation quality while reducing model size, which is valuable for real-world applications, especially in resource-constrained environments. - Sound Model Design:
Using a Bidirectional LSTM (Bi-LSTM) encoder contextualizes input sequences effectively by processing information from both past and future directions. Incorporating an attention mechanism in the decoder aligns well with contemporary model designs to improve focus on relevant source parts during translation. - Comparison to Transformer Models:
Positioning the proposed model relative to the state-of-the-art Transformer models, highlighting superior performance on the established WMT14 dataset along with smaller storage needs, underscores the practical relevance and novelty. - Comprehensive Experimental Validation:
The abstract mentions thorough experiments across multiple language pairs and comparison with traditional Seq2Seq architectures, suggesting well-rounded evaluation. - Applicability in Resource-Constrained Scenarios:
Emphasizing reduced storage requirements makes this work particularly significant for deployment in environments with limited computational resources.
Areas for Improvement:
- Quantitative Metrics and Specific Results:
The abstract does not provide concrete performance metrics such as BLEU scores or specific storage size reductions, which would help substantiate claims of superior performance and efficiency. - Details on Model Architecture Innovations:
While the architecture components are described, the precise innovative aspects or architectural changes that lead to improved results over standard Bi-LSTM with attention or Transformers are not detailed. - Text Flow and Clarity:
Some sentences could be streamlined for clarity. For example, “the experimental includes an assessment” should be “the experiments include an assessment.” Also, the phrase “maintaining a smaller size” could specify if it is model parameters, disk size, or memory footprint. - Broader Implications:
Mentioning potential real-world applications or deployment contexts beyond “resource-constrained scenarios” could enhance the perceived impact.
Overall Impression:
This abstract presents a well-motivated and practically important contribution to machine translation by combining Bi-LSTM encoding with attention in a compact Seq2Seq model that outperforms Transformers on a major benchmark. The focus on balancing accuracy with efficiency addresses clear needs in NLP deployment. Including specific performance figures and clarifying the novelty in architecture or training would strengthen the communication and credibility of the results.
