An official website of the United States government
In this paper, we introduce a morphologically-aware electronic dictionary for St. Lawrence Island Yupik, an endangered language of the Bering Strait region. Implemented using HTML, Javascript, and CSS, the dictionary is set in an uncluttered interface and permits users to search in Yupik or in English for Yupik root words and Yupik derivational suffixes. For each matching result, our electronic dictionary presents the user with the corresponding entry from the Badten (2008) Yupik-English paper dictionary. Because Yupik is a polysynthetic language, handling of multimorphemic word forms is critical. If a user searches for an inflected Yupik word form, we perform a morphological analysis and return entries for the root word and for any derivational suffixes present in the word. This electronic dictionary should serve not only as a valuable resource for all students and speakers of Yupik, but also for field linguists working towards documentation and conservation of the language.
more »
« less
IndoCollex: A Testbed for Morphological Transformation of Indonesian Word Colloquialism
Indonesian language is heavily riddled with colloquialism whether in written or spoken forms. In this paper, we identify a class of Indonesian colloquial words that have undergone morphological transformations from their standard forms, categorize their word formations, and propose a benchmark dataset of Indonesian Colloquial Lexicons (IndoCollex) consisting of informal words on Twitter expertly annotated with their standard forms and their word formation types/tags. We evalu- ate several models for character-level transduction to perform morphological word normalization on this testbed to understand their failure cases and provide baselines for future work. As IndoCollex catalogues word formation phenomena that are also present in the non-standard text of other languages, it can also provide an attractive testbed for methods tailored for cross-lingual word normalization and non-standard word formation.
more »
« less
- Award ID(s):
- 1838193
- PAR ID:
- 10291486
- Date Published:
- Journal Name:
- Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
- Page Range / eLocation ID:
- 3170 to 3183
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
In this paper, we introduce a morphologically-aware electronic dictionary for St. Lawrence Island Yupik, an endangered language of the Bering Strait region. Implemented using HTML, Javascript, and CSS, the dictionary is set in an uncluttered interface and permits users to search in Yupik or in English for Yupik root words and Yupik derivational suffixes. For each matching result, our electronic dictionary presents the user with the corresponding entry from the Badten et al. (2008) Yupik-English paper dictionary. Because Yupik is a polysynthetic language, handling of multi- morphemic word forms is critical. If a user searches for an inflected Yupik word form, we perform a morphological analysis and return entries for the root word and for any derivational suffixes present in the word. This electronic dictionary should serve not only as a valuable resource for all students and speakers of Yupik, but also for field linguists working towards documentation and conservation of the language.more » « less
-
Canonical morphological segmentation is the process of analyzing words into the standard (aka underlying) forms of their constituent morphemes.This is a core task in endangered language documentation, and NLP systems have the potential to dramatically speed up this process. In typical language documentation settings, training data for canonical morpheme segmentation is scarce, making it difficult to train high quality models. However, translation data is often much more abundant, and, in this work, we present a method that attempts to leverage translation data in the canonical segmentation task. We propose a character-level sequence-to-sequence model that incorporates representations of translations obtained from pretrained high-resource monolingual language models as an additional signal. Our model outperforms the baseline in a super-low resource setting but yields mixed results on training splits with more data. Additionally, we find that we can achieve strong performance even without needing difficult-to-obtain word level alignments. While further work is needed to make translations useful in higher-resource settings, our model shows promise in severely resource-constrained settings.more » « less
-
Motor planning forms a critical bridge between psycholinguistic and motoric models of word production. While syllables are often considered the core planning unit in speech, growing evidence hints at supra-syllabic planning, but without firm empirical support. Here, we use differential adaptation to altered auditory feedback to provide novel, straightforward evidence for word-level planning. By introducing opposing perturbations to shared segmental content (e.g., raising the first vowel formant of “sev” in “seven” while lowering it in “sever”), we assess whether participants can use the larger word context to separately oppose the two perturbations. Critically, limb control research shows that such differential learning is possible only when the shared movement forms part of separate motor plans. We found differential adaptation in multisyllabic words, but of smaller size relative to monosyllabic words. These results strongly suggest speech relies on an interactive motor planning process encompassing both syllables and words.more » « less
-
We present end-to-end neural models for detecting metaphorical word use in context. We show that relatively standard BiLSTM models which operate on complete sentences work well in this setting, in comparison to previous work that used more restricted forms of linguistic context. These models establish a new state-of-the-art on existing verb metaphor detection benchmarks, and show strong performance on jointly predicting the metaphoricity of all words in a running text.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2021.findings-acl.280
