Transductive data-selection algorithms for fine-tuning neural machine translation

Machine Translation models are trained to translate a variety of documents from one language into another. However, models specifically trained for a particular characteristics of the documents tend to perform better. Fine-tuning is a technique for adapting an NMT model to some domain. In this work, we want to use this technique to adapt the model to a given test set. In particular, we are using transductive data selection algorithms which take advantage the information of the test set to retrieve sentences from a larger parallel set

Adaptation of machine translation models with back-translated data using transductive data selection methods

Data selection has proven its merit for improving Neural Machine Translation (NMT), when applied to authentic data. But the benefit of using synthetic data in NMT training, produced by the popular back-translation technique, raises the question if data selection could also be useful for synthetic data? In this work we use Infrequent n-gram Recovery (INR) and Feature Decay Algorithms (FDA), two transductive data selection methods to obtain subsets of sentences from synthetic data. These methods ensure that selected sentences share n-grams with the test set so the NMT model can be adapted to translate it. Performing data selection on back-translated data creates new challenges as the source-side may contain noise originated by the model used in the back-translation. Hence, finding ngrams present in the test set become more difficult. Despite that, in our work we show that adapting a model with a selection of synthetic data is an useful approach

Data selection with feature decay algorithms using an approximated target side

AbstractData selection techniques applied to neural machine trans-lation (NMT) aim to increase the performance of a model byretrieving a subset of sentences for use as training data.One of the possible data selection techniques are trans-ductive learning methods, which select the data based on thetest set, i.e. the document to be translated. A limitation ofthese methods to date is that using the source-side test setdoes not by itself guarantee that sentences are selected withcorrect translations, or translations that are suitable given thetest-set domain. Some corpora, such as subtitle corpora, maycontain parallel sentences with inaccurate translations causedby localization or length restrictions.In order to try to fix this problem, in this paper we pro-pose to use an approximated target-side in addition to thesource-side when selecting suitable sentence-pairs for train-ing a model. This approximated target-side is built by pre-translating the source-side.In this work, we explore the performance of this generalidea for one specific data selection approach called FeatureDecay Algorithms (FDA).We train German-English NMT models on data selectedby using the test set (source), the approximated target side,and a mixture of both. Our findings reveal that models builtusing a combination of outputs of FDA (using the test setand an approximated target side) perform better than thosesolely using the test set. We obtain a statistically significantimprovement of more than 1.5 BLEU points over a modeltrained with all data, and more than 0.5 BLEU points over astrong FDA baseline that uses source-side information only

Feature decay algorithms for neural machine translation

Neural Machine Translation (NMT) systems require a lot of data to be competitive. For this reason, data selection techniques are used only for finetuning systems that have been trained with larger amounts of data. In this work we aim to use Feature Decay Algorithms (FDA) data selection techniques not only to fine-tune a system but also to build a complete system with less data. Our findings reveal that it is possible to find a subset of sentence pairs, that outperforms by 1.11 BLEU points the full training corpus, when used for training a German-English NMT system

Data selection with feature decay algorithms using an approximated target side

Data selection techniques applied to neural machine translation (NMT) aim to increase the performance of a model by retrieving a subset of sentences for use as training data. One of the possible data selection techniques are transductive learning methods, which select the data based on the test set, i.e. the document to be translated. A limitation of these methods to date is that using the source-side test set does not by itself guarantee that sentences are selected with correct translations, or translations that are suitable given the test-set domain. Some corpora, such as subtitle corpora, may contain parallel sentences with inaccurate translations caused by localization or length restrictions. In order to try to fix this problem, in this paper we propose to use an approximated target-side in addition to the source-side when selecting suitable sentence-pairs for training a model. This approximated target-side is built by pretranslating the source-side. In this work, we explore the performance of this general idea for one specific data selection approach called Feature Decay Algorithms (FDA). We train German-English NMT models on data selected by using the test set (source), the approximated target side, and a mixture of both. Our findings reveal that models built using a combination of outputs of FDA (using the test set and an approximated target side) perform better than those solely using the test set. We obtain a statistically significant improvement of more than 1.5 BLEU points over a model trained with all data, and more than 0.5 BLEU points over a strong FDA baseline that uses source-side information only

Elastic-substitution decoding for hierarchical SMT: efficiency, richer search and double labels

Elastic-substitution decoding (ESD), first introduced by Chiang (2010), can be important for obtaining good results when applying labels to enrich hierarchical statistical machine translation (SMT). However, an efficient implementation is essential for scalable application. We describe how to achieve this, contributing essential details that were missing in the original exposition. We compare ESD to strict matching and show its superiority for both reordering and syntactic labels. To overcome the sub-optimal performance due to the late evaluation of features marking label substitution types, we increase the diversity of the rules explored during cube pruning initialization with respect to labels their labels. This approach gives significant improvements over basic ESD and performs favorably compared to extending the search by increasing the cube pruning pop-limit. Finally, we look at combining multiple labels. The combination of reordering labels and target-side boundary-tags yields a significant improvement in terms of the word-order sensitive metrics Kendall reordering score and METEOR. This confirms our intuition that the combination of reordering labels and syntactic labels can yield improvements over either label by itself, despite increased sparsity

SChuBERT:Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction

Predicting the number of citations of scholarly documents is an upcoming task in scholarly document processing. Besides the intrinsic merit of this information, it also has a wider use as an imperfect proxy for quality which has the advantage of being cheaply available for large volumes of scholarly documents. Previous work has dealt with number of citations prediction with relatively small training data sets, or larger datasets but with short, incomplete input text. In this work we leverage the open access ACL Anthology collection in combination with the Semantic Scholar bibliometric database to create a large corpus of scholarly documents with associated citation information and we propose a new citation prediction model called SChuBERT. In our experiments we compare SChuBERT with several state-of-the-art citation prediction models and show that it outperforms previous methods by a large margin. We also show the merit of using more training data and longer input for number of citations prediction.Comment: Published at the First Workshop on Scholarly Document Processing, at EMNLP 2020. Minor corrections were made to the workshop version, including addition of color to Figures 1,