Existing text selection techniques on touchscreen focus on improving the
control for moving the carets. Coarse-grained text selection on word and phrase
levels has not received much support beyond word-snapping and entity
recognition. We introduce 1D-Touch, a novel text selection method that
complements the carets-based sub-word selection by facilitating the selection
of semantic units of words and above. This method employs a simple vertical
slide gesture to expand and contract a selection area from a word. The
expansion can be by words or by semantic chunks ranging from sub-phrases to
sentences. This technique shifts the concept of text selection, from defining a
range by locating the first and last words, towards a dynamic process of
expanding and contracting a textual semantic entity. To understand the effects
of our approach, we prototyped and tested two variants: WordTouch, which offers
a straightforward word-by-word expansion, and ChunkTouch, which leverages NLP
to chunk text into syntactic units, allowing the selection to grow by
semantically meaningful units in response to the sliding gesture. Our
evaluation, focused on the coarse-grained selection tasks handled by 1D-Touch,
shows a 20% improvement over the default word-snapping selection method on
Android.

1D-Touch 是一种新颖的文本选择方法，通过垂直滑动手势从单词开始扩展和收缩选择区域，使得选择的范围可以根据语义单元从子短语扩展至句子，相较于默认的单词捕捉选择方法，在 Android 上处理粗粒度选择任务上有 20% 的改善。

1D-Touch: 利用半直接手势辅助的 NLP 粗糙文本选择

1D-Touch: NLP-Assisted Coarse Text Selection via a Semi-Direct Gesture

Conventional neural machine translation (NMT) models typically use subwords
and words as the basic units for model input and comprehension. However,
complete words and phrases composed of several tokens are often the fundamental
units for expressing semantics, referred to as semantic units. To address this
issue, we propose a method Semantic Units for Machine Translation (SU4MT) which
models the integral meanings of semantic units within a sentence, and then
leverages them to provide a new perspective for understanding the sentence.
Specifically, we first propose Word Pair Encoding (WPE), a phrase extraction
method to help identify the boundaries of semantic units. Next, we design an
Attentive Semantic Fusion (ASF) layer to integrate the semantics of multiple
subwords into a single vector: the semantic unit representation. Lastly, the
semantic-unit-level sentence representation is concatenated to the token-level
one, and they are combined as the input of encoder. Experimental results
demonstrate that our method effectively models and leverages
semantic-unit-level information and outperforms the strong baselines. The code
is available at this https URL

我们提出了一种 Semantic Units for Machine Translation (SU4MT) 方法，通过模型化句子中的语义单元，并利用它们来为理解句子提供新的视角。实验结果表明，我们的方法能有效地建模和利用语义单元级别的信息，并优于强基线模型。

利用语义单元增强神经机器翻译

Enhancing Neural Machine Translation with Semantic Units

In this paper, we develop a MultiTask Learning (MTL) model to achieve dense
predictions for comics panels to, in turn, facilitate the transfer of comics
from one publication channel to another by assisting authors in the task of
reconfiguring their narratives. Our MTL method can successfully identify the
semantic units as well as the embedded notion of 3D in comic panels. This is a
significantly challenging problem because comics comprise disparate artistic
styles, illustrations, layouts, and object scales that depend on the authors
creative process. Typically, dense image-based prediction techniques require a
large corpus of data. Finding an automated solution for dense prediction in the
comics domain, therefore, becomes more difficult with the lack of ground-truth
dense annotations for the comics images. To address these challenges, we
develop the following solutions: 1) we leverage a commonly-used strategy known
as unsupervised image-to-image translation, which allows us to utilize a large
corpus of real-world annotations; 2) we utilize the results of the translations
to develop our multitasking approach that is based on a vision transformer
backbone and a domain transferable attention module; 3) we study the
feasibility of integrating our MTL dense-prediction method with an existing
retargeting method, thereby reconfiguring comics.

本文提出了一种 MTL 模型，以实现对漫画画板的密集预测，从而帮助作者重新组织他们的叙述，并探讨了与现有方法整合的可行性。

密集多任务学习以重新配置连环漫画

Dense Multitask Learning to Reconfigure Comics

Multi-modal neural machine translation (NMT) aims to translate source
sentences into a target language paired with images. However, dominant
multi-modal NMT models do not fully exploit fine-grained semantic
correspondences between semantic units of different modalities, which have
potential to refine multi-modal representation learning. To deal with this
issue, in this paper, we propose a novel graph-based multi-modal fusion encoder
for NMT. Specifically, we first represent the input sentence and image using a
unified multi-modal graph, which captures various semantic relationships
between multi-modal semantic units (words and visual objects). We then stack
multiple graph-based multi-modal fusion layers that iteratively perform
semantic interactions to learn node representations. Finally, these
representations provide an attention-based context vector for the decoder. We
evaluate our proposed encoder on the Multi30K datasets. Experimental results
and in-depth analysis show the superiority of our multi-modal NMT model.

本论文提出了一种新型的基于图的多模态融合编码器，用于多模态神经机器翻译（NMT）中利用不同模态语义单元之间的细粒度语义对应关系，以优化多模态表示学习，并在 Multi30K 数据集上验证了模型的卓越性能。