A common intermediate language representation or an interlingua is the holy grail in machine translation. Thanks to the new neural machine translation approach, it seems that there are good perspectives towards this goal. In this paper, we propose a new architecture based on introducing an interlingua loss as an additional training objective. By adding and forcing this interlingua loss, we are able to train multiple encoders and decoders for each language, sharing a common intermediate representation. Preliminary translation results on the WMT Turkish English and WMT 2019 Kazakh English tasks show improvements over the baseline system. Additionally, since the final objective of our architecture is having compatible encoder decoders based on a common representation, we visualize and evaluate the learned intermediate representations. What is most relevant from our study is that our architecture shows the benefits of the dreamed interlingua since it is capable of: (1) reducing the number of production systems, with respect to the number of languages, from quadratic to linear (2) incrementally adding a new language in the system without retraining languages previously there and (3) allowing for translations from the new language to all the others present in the system
While unsupervised MT @cite_33 @cite_32 is not directly pursuing a common intermediate representation, but it is somehow related to our approach. (2017) and (2017) propose a translation system that is able to translate trained only on a monolingual corpus. The architecture is basically a shared encoder with pre-trained embeddings and two decoders (one of them includes an autoencoder). On the other hand, our work is also related to recent works on sentence representations @cite_1 @cite_15 @cite_11 . However, the main difference is that these works aim at extending representations to other natural language processing tasks, while we are aiming at finding the most suitable representation to make interlingua machine translation feasible. While interesting for further research, it is out-of-scope of this study the evaluation and adaptation of this intermediate representation to multiple tasks.
@cite_33 @cite_1 @cite_32 @cite_15 @cite_11
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Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the STS Benchmark is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012-2017). This work focuses on the task of finding latent vector representations of the words in a corpus. In particular, we address the issue of what to do when there are multiple languages in the corpus. Prior work has, among other techniques, used canonical correlation analysis to project pre-trained vectors in two languages into a common space. We propose a simple and scalable method that is inspired by the notion that the learned vector representations should be invariant to translation between languages. We show empirically that our method outperforms prior work on multilingual tasks, matches the performance of prior work on monolingual tasks, and scales linearly with the size of the input data (and thus the number of languages being embedded). A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture. However, little is known about what these models learn about source and target languages during the training process. In this work, we analyze the representations learned by neural MT models at various levels of granularity and empirically evaluate the quality of the representations for learning morphology through extrinsic part-of-speech and morphological tagging tasks. We conduct a thorough investigation along several parameters: word-based vs. character-based representations, depth of the encoding layer, the identity of the target language, and encoder vs. decoder representations. Our data-driven, quantitative evaluation sheds light on important aspects in the neural MT system and its ability to capture word structure.