Miguel Ballesteros, Rishita Anubhai, Shuai Wang, Nima Pourdamghani, Yogarshi Vyas, Jie Ma, Parminder Bhatia, Kathleen McKeown, Yaser Al-Onaizan. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.

Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation.In this work we explore this intuition by breaking translation into a two step process: generating a rough gloss by means of a dictionary and then 'translating' the resulting pseudo-translation, or 'Translationese' into a fully fluent translation.We build our Translationese decoder once from a mish-mash of parallel data that has the target language in common and then can build dictionaries on demand using unsupervised techniques, resulting in rapidly generated unsupervised neural MT systems for many source languages.We apply this process to 14 test languages, obtaining better or comparable translation results on high-resource languages than previously published unsupervised MT studies, and obtaining good quality results for low-resource languages that have never been used in an unsupervised MT scenario.

We present a method for translating texts between close language pairs.The method does not require parallel data, and it does not require the languages to be written in the same script.We show results for six language pairs: Afrikaans/Dutch, Bosnian/Serbian, Danish/Swedish, Macedonian/Bulgarian, Malaysian/Indonesian, and Polish/Belorussian.We report BLEU scores showing our method to outperform others that do not use parallel data.

When a large-scale incident or disaster occurs, there is often a great demand for rapidly developing a system to extract detailed and new information from lowresource languages (LLs).We propose a novel approach to discover comparable documents in high-resource languages (HLs), and project Entity Discovery and Linking results from HLs documents back to LLs.We leverage a wide variety of language-independent forms from multiple data modalities, including image processing (image-to-image retrieval, visual similarity and face recognition) and sound matching.We also propose novel methods to learn entity priors from a large-scale HL corpus and knowledge base.Using Hausa and Chinese as the LLs and English as the HL, experiments show that our approach achieves 36.1% higher Hausa name tagging F-score over a costly supervised model, and 9.4% higher Chineseto-English Entity Linking accuracy over state-of-the-art.

We present a method for generating English sentences from Abstract Meaning Representation (AMR) graphs, exploiting a parallel corpus of AMRs and English sentences.We treat AMR-to-English generation as phrase-based machine translation (PBMT).We introduce a method that learns to linearize tokens of AMR graphs into an English-like order.Our linearization reduces the amount of distortion in PBMT and increases generation quality.We report a Bleu score of 26.8 on the standard AMR/English test set.

We align pairs of English sentences and corresponding Abstract Meaning Repre-sentations (AMR), at the token level. Such alignments will be useful for downstream extraction of semantic interpretation and generation rules. Our method involves linearizing AMR structures and perform-ing symmetrized EM training. We obtain 86.5 % and 83.1 % alignment F score on de-velopment and test sets. 1

We have developed a Gaussian Process Regression method with adaptive kernels for concealment of the missing macro-blocks of block-based video compression schemes in a packet video system. Despite promising results, the proposed algorithm introduces a solid framework for further improvements. In this paper, the problem of estimating lost macro-blocks will be solved by estimating the proper covariance function of the Gaussian process defined over a region around the missing macro-blocks (i.e. its kernel function). In order to preserve block edges, the kernel is constructed adaptively by using the local edge related information. Moreover, we can achieve more improvement by local estimation of the kernel parameters. While restoring the prominent edges of the missing macro-blocks, the proposed method produces perceptually smooth concealed frames. Objective and subjective evaluations verify the effectiveness of the proposed method.