Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userTowards New Generation Translation Memory Systems
نحو أنظمة ذاكرة الترجمة الجيل الجديدة
179
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
على الرغم من شعبية هائلة لأنظمة ذاكرة الترجمة والبحث النشط في هذا المجال، لا تزال ميزات معالجة اللغة الخاصة بها تعاني من قيود معينة.في حين أن العديد من الأوراق الأخيرة تركز على قدرات مطابقة الدلالية من TMS، فإن هذه الدراسة المخططة ستعالج كيفية أداء هذه الأدوات عند التعامل مع شرائح أطول وما إذا كان هذا قد يكون سببا لدرجات مطابقة أقل.سيتم تنفيذ تجربة على Corpora من مجالات مختلفة (متكررة).بعد النتائج، سيتم تقديم توصيات للتطورات المستقبلية من TMS الجديدة.
Despite the enormous popularity of Translation Memory systems and the active research in the field, their language processing features still suffer from certain limitations. While many recent papers focus on semantic matching capabilities of TMs, this planned study will address how these tools perform when dealing with longer segments and whether this could be a cause of lower match scores. An experiment will be carried out on corpora from two different (repetitive) domains. Following the results, recommendations for future developments of new TMs will be made.
References used
https://aclanthology.org/
rate research
Read More
Translation Memory (TM) system, a major component of computer-assisted translation (CAT), is widely used to improve human translators' productivity by making effective use of previously translated resource. We propose a method to achieve high-speed r
etrieval from a large translation memory by means of similarity evaluation based on vector model, and present the experimental result. Through our experiment using Lucene, an open source information retrieval search engine, we conclude that it is possible to achieve real-time retrieval speed of about tens of microseconds even for a large translation memory with 5 million segment pairs.
Neural table-to-text generation models have achieved remarkable progress on an array of tasks. However, due to the data-hungry nature of neural models, their performances strongly rely on large-scale training examples, limiting their applicability in
real-world applications. To address this, we propose a new framework: Prototype-to-Generate (P2G), for table-to-text generation under the few-shot scenario. The proposed framework utilizes the retrieved prototypes, which are jointly selected by an IR system and a novel prototype selector to help the model bridging the structural gap between tables and texts. Experimental results on three benchmark datasets with three state-of-the-art models demonstrate that the proposed framework significantly improves the model performance across various evaluation metrics.
The lack of labeled training data for new features is a common problem in rapidly changing real-world dialog systems. As a solution, we propose a multilingual paraphrase generation model that can be used to generate novel utterances for a target feat
ure and target language. The generated utterances can be used to augment existing training data to improve intent classification and slot labeling models. We evaluate the quality of generated utterances using intrinsic evaluation metrics and by conducting downstream evaluation experiments with English as the source language and nine different target languages. Our method shows promise across languages, even in a zero-shot setting where no seed data is available.
This paper describes our system (Team ID: nictrb) for participating in the WAT'21 restricted machine translation task. In our submitted system, we designed a new training approach for restricted machine translation. By sampling from the translation t
arget, we can solve the problem that ordinary training data does not have a restricted vocabulary. With the further help of constrained decoding in the inference phase, we achieved better results than the baseline, confirming the effectiveness of our solution. In addition, we also tried the vanilla and sparse Transformer as the backbone network of the model, as well as model ensembling, which further improved the final translation performance.
This paper describes NiuTrans neural machine translation systems of the WMT 2021 news translation tasks. We made submissions to 9 language directions, including English2Chinese, Japanese, Russian, Icelandic and English2Hausa tasks. Our primary system
s are built on several effective variants of Transformer, e.g., Transformer-DLCL, ODE-Transformer. We also utilize back-translation, knowledge distillation, post-ensemble, and iterative fine-tuning techniques to enhance the model performance further.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
