Cognates are variants of the same lexical form across different languages;
for example 'fonema' in Spanish and 'phoneme' in English are cognates, both of
which mean 'a unit of sound'. The task of automatic detection of cognates among
any two languages can help downstream NLP tasks such as Cross-lingual
Information Retrieval, Computational Phylogenetics, and Machine Translation. In
this paper, we demonstrate the use of cross-lingual word embeddings for
detecting cognates among fourteen Indian Languages. Our approach introduces the
use of context from a knowledge graph to generate improved feature
representations for cognate detection. We, then, evaluate the impact of our
cognate detection mechanism on neural machine translation (NMT), as a
downstream task. We evaluate our methods to detect cognates on a challenging
dataset of twelve Indian languages, namely, Sanskrit, Hindi, Assamese, Oriya,
Kannada, Gujarati, Tamil, Telugu, Punjabi, Bengali, Marathi, and Malayalam.
Additionally, we create evaluation datasets for two more Indian languages,
Konkani and Nepali. We observe an improvement of up to 18% points, in terms of
F-score, for cognate detection. Furthermore, we observe that cognates extracted
using our method help improve NMT quality by up to 2.76 BLEU. We also release
our code, newly constructed datasets and cross-lingual models publicly.

使用跨语言词向量检测印度 14 种语言的同源词，通过知识图谱生成上下文特征表示以提高同源词检测方法，并在 12 种印度语言和 2 种新语言上评估此方法，获得最高 18% 的 F-score 和 2.76 BLEU 的 NMT 改进，最后公开代码和数据集。

利用跨语言特征提高低资源语言同源词检测的效果

Harnessing Cross-lingual Features to Improve Cognate Detection for  Low-resource Languages

Automatic detection of cognates helps downstream NLP tasks of Machine
Translation, Cross-lingual Information Retrieval, Computational Phylogenetics
and Cross-lingual Named Entity Recognition. Previous approaches for the task of
cognate detection use orthographic, phonetic and semantic similarity based
features sets. In this paper, we propose a novel method for enriching the
feature sets, with cognitive features extracted from human readers' gaze
behaviour. We collect gaze behaviour data for a small sample of cognates and
show that extracted cognitive features help the task of cognate detection.
However, gaze data collection and annotation is a costly task. We use the
collected gaze behaviour data to predict cognitive features for a larger sample
and show that predicted cognitive features, also, significantly improve the
task performance. We report improvements of 10% with the collected gaze
features, and 12% using the predicted gaze features, over the previously
proposed approaches. Furthermore, we release the collected gaze behaviour data
along with our code and cross-lingual models.

本文提出了一种新的方法，利用人类阅读者的凝视行为提取认知特征，用于自动检测同源词，并通过使用搜集到的和预测的视线行为数据，证明该方法可以帮助提高同源词检测任务的性能提高 10%，相比之前的方法预测视线行为数据的性能提高了 12%。

认知感知同源词检测

Cognition-aware Cognate Detection

We propose the neural string edit distance model for string-pair matching and
string transduction based on learnable string edit distance. We modify the
original expectation-maximization learned edit distance algorithm into a
differentiable loss function, allowing us to integrate it into a neural network
providing a contextual representation of the input. We evaluate on cognate
detection, transliteration, and grapheme-to-phoneme conversion, and show that
we can trade off between performance and interpretability in a single
framework. Using contextual representations, which are difficult to interpret,
we match the performance of state-of-the-art string-pair matching models. Using
static embeddings and a slightly different loss function, we force
interpretability, at the expense of an accuracy drop.

本文提出神经字符串编辑距离模型用于字符串对匹配与转录，并在同一框架中平衡精度与可解释性，通过对认知相似性，音译以及字音转换等任务进行评估达到了最先进匹配模型的表现。

神经字符串编辑距离

Neural String Edit Distance

Ranking functions in information retrieval are often used in search engines
to recommend the relevant answers to the query. This paper makes use of this
notion of information retrieval and applies onto the problem domain of cognate
detection. The main contributions of this paper are: (1) positional
segmentation, which incorporates the sequential notion; (2) graphical error
modelling, which deduces the transformations. The current research work focuses
on classification problem; which is distinguishing whether a pair of words are
cognates. This paper focuses on a harder problem, whether we could predict a
possible cognate from the given input. Our study shows that when language
modelling smoothing methods are applied as the retrieval functions and used in
conjunction with positional segmentation and error modelling gives better
results than competing baselines, in both classification and prediction of
cognates.
Source code is at: this https URL

本篇论文利用信息检索中的排名函数，应用于同源检测，针对同源检测的难点，使用语言模型平滑方法和位置分割与错误建模技术，相比其他基线方法在分类和预测中表现更佳。