利用计算工具测量罗曼语系语言间的跨语言可懂性

Are Mutually Intelligible Languages Easier to Translate?

Two languages are considered mutually intelligible if their native speakers can communicate with each other, while using their own mother tongue. How does the fact that humans perceive a language pair as mutually intelligible affect the ability to learn a translation model between them? We hypothesize that the amount of data needed to train a neural ma-chine translation model is anti-proportional to the languages' mutual intelligibility. Experiments on the Romance language group reveal that there is indeed strong correlation between the area under a model's learning curve and mutual intelligib…

QuanLing：西罗曼语语言距离量化的跨分支验证

QuanLing: Cross-Branch Validation of Language Distance Quantification on Western Romance

本文通过将QuanLing框架扩展到西罗曼语支（法、葡、西、意），验证了其跨分支适用性，发现葡西最接近、法意最远，且与北日耳曼语结果一致，证明其泛化能力。

A Computational Approach to Measuring the Semantic Divergence of Cognates

Meaning is the foundation stone of intercultural communication. Languages are continuously changing, and words shift their meanings for various reasons. Semantic divergence in related languages is a key concern of historical linguistics. In this paper we investigate semantic divergence across languages by measuring the semantic similarity of cognate sets in multiple languages. The method that we propose is based on cross-lingual word embeddings. In this paper we implement and evaluate our method on English and five Romance languages, but it can be extended easily to any language pair, requirin…

通过声学掩蔽量化辅音对单词可懂度的贡献

Quantifying Consonant Contributions to Word Intelligibility via Acoustic Masking

本文提出基于声学掩蔽的可扩展方法，通过自动语音识别模型计算掩蔽诱导误识别率来量化辅音对单词可懂度的贡献，并验证其与音素频率和功能负荷的相关性，发现辅音贡献具有语言依赖性。

Measuring Gender Bias in Word Embeddings of Gendered Languages Requires Disentangling Grammatical Gender Signals

Does the grammatical gender of a language interfere when measuring the semantic gender information captured by its word embeddings? A number of anomalous gender bias measurements in the embeddings of gendered languages suggest this possibility. We demonstrate that word embeddings learn the association between a noun and its grammatical gender in grammatically gendered languages, which can skew social gender bias measurements. Consequently, word embedding post-processing methods are introduced to quantify, disentangle, and evaluate grammatical gender signals. The evaluation is performed on five…