15,537 research outputs found
Translating pro-drop languages with reconstruction models
Pronouns are frequently omitted in pro-drop languages, such as Chinese, generally leading to significant challenges with respect to the production of complete translations. To date, very little attention has been paid to the dropped pronoun (DP) problem within neural machine translation (NMT). In this work, we propose a novel reconstruction-based approach to alleviating DP translation problems for NMT models. Firstly, DPs within all source sentences are automatically annotated with parallel information extracted from the bilingual training corpus. Next, the annotated source sentence is reconstructed from hidden representations in the NMT model. With auxiliary training objectives, in the terms of reconstruction scores, the parameters associated with the NMT model are guided to produce enhanced hidden representations that are encouraged as much as possible to embed annotated DP information. Experimental results on both Chinese-English and Japanese-English dialogue translation tasks show that the proposed approach significantly and consistently improves translation performance over a strong NMT baseline, which is directly built on the training data annotated with DPs
On the Reproducibility and Generalisation of the Linear Transformation of Word Embeddings
Linear transformation is a way to learn a linear relationship between two word embeddings, such that words in the two different embedding spaces can be semantically related. In this paper, we examine the reproducibility and generalisation of the linear transformation of word embeddings. Linear transformation is particularly useful when translating word embedding models in different languages, since it can capture the semantic relationships between two models. We first reproduce two linear transformation approaches, a recent one using orthogonal transformation and the original one using simple matrix transformation. Previous findings on a machine translation task are re-examined, validating that linear transformation is indeed an effective way to transform word embedding models in different languages. In particular, we show that the orthogonal transformation can better relate the different embedding models. Following the verification of previous findings, we then study the generalisation of linear transformation in a multi-language Twitter election classification task. We observe that the orthogonal transformation outperforms the matrix transformation. In particular, it significantly outperforms the random classifier by at least 10% under the F1 metric across English and Spanish datasets. In addition, we also provide best practices when using linear transformation for multi-language Twitter election classification
ImageJ2: ImageJ for the next generation of scientific image data
ImageJ is an image analysis program extensively used in the biological
sciences and beyond. Due to its ease of use, recordable macro language, and
extensible plug-in architecture, ImageJ enjoys contributions from
non-programmers, amateur programmers, and professional developers alike.
Enabling such a diversity of contributors has resulted in a large community
that spans the biological and physical sciences. However, a rapidly growing
user base, diverging plugin suites, and technical limitations have revealed a
clear need for a concerted software engineering effort to support emerging
imaging paradigms, to ensure the software's ability to handle the requirements
of modern science. Due to these new and emerging challenges in scientific
imaging, ImageJ is at a critical development crossroads.
We present ImageJ2, a total redesign of ImageJ offering a host of new
functionality. It separates concerns, fully decoupling the data model from the
user interface. It emphasizes integration with external applications to
maximize interoperability. Its robust new plugin framework allows everything
from image formats, to scripting languages, to visualization to be extended by
the community. The redesigned data model supports arbitrarily large,
N-dimensional datasets, which are increasingly common in modern image
acquisition. Despite the scope of these changes, backwards compatibility is
maintained such that this new functionality can be seamlessly integrated with
the classic ImageJ interface, allowing users and developers to migrate to these
new methods at their own pace. ImageJ2 provides a framework engineered for
flexibility, intended to support these requirements as well as accommodate
future needs
Document-Level Machine Translation with Large Language Models
Large language models (LLMs) such as Chat-GPT can produce coherent, cohesive,
relevant, and fluent answers for various natural language processing (NLP)
tasks. Taking document-level machine translation (MT) as a testbed, this paper
provides an in-depth evaluation of LLMs' ability on discourse modeling. The
study fo-cuses on three aspects: 1) Effects of Discourse-Aware Prompts, where
we investigate the impact of different prompts on document-level translation
quality and discourse phenomena; 2) Comparison of Translation Models, where we
compare the translation performance of Chat-GPT with commercial MT systems and
advanced document-level MT methods; 3) Analysis of Discourse Modelling
Abilities, where we further probe discourse knowledge encoded in LLMs and
examine the impact of training techniques on discourse modeling. By evaluating
a number of benchmarks, we surprisingly find that 1) leveraging their powerful
long-text mod-eling capabilities, ChatGPT outperforms commercial MT systems in
terms of human evaluation. 2) GPT-4 demonstrates a strong ability to explain
discourse knowledge, even through it may select incorrect translation
candidates in contrastive testing. 3) ChatGPT and GPT-4 have demonstrated
superior performance and show potential to become a new and promising paradigm
for document-level translation. This work highlights the challenges and
opportunities of discourse modeling for LLMs, which we hope can inspire the
future design and evaluation of LLMs
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