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Register a new userPhylogeny and geometry of languages from normalized Levenshtein distance
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Maurizio Serva
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The idea that the distance among pairs of languages can be evaluated from lexical differences seems to have its roots in the work of the French explorer Dumont DUrville. He collected comparative words lists of various languages during his voyages aboard the Astrolabe from 1826 to 1829 and, in his work about the geographical division of the Pacific, he proposed a method to measure the degree of relation between languages. The method used by the modern lexicostatistics, developed by Morris Swadesh in the 1950s, measures distances from the percentage of shared cognates, which are words with a common historical origin. The weak point of this method is that subjective judgment plays a relevant role. Recently, we have proposed a new automated method which is motivated by the analogy with genetics. The new approach avoids any subjectivity and results can be easily replicated by other scholars. The distance between two languages is defined by considering a renormalized Levenshtein distance between pair of words with the same meaning and averaging on the words contained in a list. The renormalization, which takes into account the length of the words, plays a crucial role, and no sensible results can be found without it. In this paper we give a short review of our automated method and we illustrate it by considering the cluster of Malagasy dialects. We show that it sheds new light on their kinship relation and also that it furnishes a lot of new information concerning the modalities of the settlement of Madagascar.
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The dialects of Madagascar belong to the Greater Barito East group of the Austronesian family and it is widely accepted that the Island was colonized by Indonesian sailors after a maritime trek which probably took place around 650 CE. The language most closely related to Malagasy dialects is Maanyan but also Malay is strongly related especially for what concerns navigation terms. Since the Maanyan Dayaks live along the Barito river in Kalimantan (Borneo) and they do not possess the necessary skill for long maritime navigation, probably they were brought as subordinates by Malay sailors. In a recent paper we compared 23 different Malagasy dialects in order to determine the time and the landing area of the first colonization. In this research we use new data and new methods to confirm that the landing took place on the south-east coast of the Island. Furthermore, we are able to state here that it is unlikely that there were multiple settlements and, therefore, colonization consisted in a single founding event. To reach our goal we find out the internal kinship relations among all the 23 Malagasy dialects and we also find out the different kinship degrees of the 23 dialects versus Malay and Maanyan. The method used is an automated version of the lexicostatistic approach. The data concerning Madagascar were collected by the author at the beginning of 2010 and consist of Swadesh lists of 200 items for 23 dialects covering all areas of the Island. The lists for Maanyan and Malay were obtained from published datasets integrated by authors interviews.
Conversational Question Simplification (CQS) aims to simplify self-contained questions into conversational ones by incorporating some conversational characteristics, e.g., anaphora and ellipsis. Existing maximum likelihood estimation (MLE) based methods often get trapped in easily learned tokens as all tokens are treated equally during training. In this work, we introduce a Reinforcement Iterative Sequence Editing (RISE) framework that optimizes the minimum Levenshtein distance (MLD) through explicit editing actions. RISE is able to pay attention to tokens that are related to conversational characteristics. To train RISE, we devise an Iterative Reinforce Training (IRT) algorithm with a Dynamic Programming based Sampling (DPS) process to improve exploration. Experimental results on two benchmark datasets show that RISE significantly outperforms state-of-the-art methods and generalizes well on unseen data.
There is a great deal of work in cognitive psychology, linguistics, and computer science, about using word (or phrase) frequencies in context in text corpora to develop measures for word similarity or word association, going back to at least the 1960s. The goal of this chapter is to introduce the normalizedis a general way to tap the amorphous low-grade knowledge available for free on the Internet, typed in by local users aiming at personal gratification of diverse objectives, and yet globally achieving what is effectively the largest semantic electronic database in the world. Moreover, this database is available for all by using any search engine that can return aggregate page-count estimates for a large range of search-queries. In the paper introducing the NWD it was called `normalized Google distance (NGD), but since Google doesnt allow computer searches anymore, we opt for the more neutral and descriptive NWD. web distance (NWD) method to determine similarity between words and phrases. It
The normalized information distance is a universal distance measure for objects of all kinds. It is based on Kolmogorov complexity and thus uncomputable, but there are ways to utilize it. First, compression algorithms can be used to approximate the Kolmogorov complexity if the objects have a string representation. Second, for names and abstract concepts, page count statistics from the World Wide Web can be used. These practical realizations of the normalized information distance can then be applied to machine learning tasks, expecially clustering, to perform feature-free and parameter-free data mining. This chapter discusses the theoretical foundations of the normalized information distance and both practical realizations. It presents numerous examples of successful real-world applications based on these distance measures, ranging from bioinformatics to music clustering to machine translation.
We propose a novel scheme to use the Levenshtein Transformer to perform the task of word-level quality estimation. A Levenshtein Transformer is a natural fit for this task: trained to perform decoding in an iterative manner, a Levenshtein Transformer can learn to post-edit without explicit supervision. To further minimize the mismatch between the translation task and the word-level QE task, we propose a two-stage transfer learning procedure on both augmented data and human post-editing data. We also propose heuristics to construct reference labels that are compatible with subword-level finetuning and inference. Results on WMT 2020 QE shared task dataset show that our proposed method has superior data efficiency under the data-constrained setting and competitive performance under the unconstrained setting.
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