Morphological Inflection
37 papers with code • 0 benchmarks • 1 datasets
Morphological Inflection is the task of generating a target (inflected form) word from a source word (base form), given a morphological attribute, e.g. number, tense, and person etc. It is useful for alleviating data sparsity issues in translating morphologically rich languages. The transformation from a base form to an inflected form usually includes concatenating the base form with a prefix or a suffix and substituting some characters. For example, the inflected form of a Finnish stem eläkeikä (retirement age) is eläkeiittä when the case is abessive and the number is plural.
Source: Tackling Sequence to Sequence Mapping Problems with Neural Networks
Benchmarks
These leaderboards are used to track progress in Morphological Inflection
UniMorph 4.0
Latest papers with no code
Do RNN States Encode Abstract Phonological Processes?
Sequence-to-sequence models have delivered impressive results in word formation tasks such as morphological inflection, often learning to model subtle morphophonological details with limited training data.
Searching for Search Errors in Neural Morphological Inflection
Yet, on word-level tasks, exact inference of these models reveals the empty string is often the global optimum.
Noise Isn't Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models
Morphological inflection, like many sequence-to-sequence tasks, sees great performance from recurrent neural architectures when data is plentiful, but performance falls off sharply in lower-data settings.
Linguistically inspired morphological inflection with a sequence to sequence model
Inflection is an essential part of every human language's morphology, yet little effort has been made to unify linguistic theory and computational methods in recent years.
Ensemble Self-Training for Low-Resource Languages: Grapheme-to-Phoneme Conversion and Morphological Inflection
We present an iterative data augmentation framework, which trains and searches for an optimal ensemble and simultaneously annotates new training data in a self-training style.
The CMU-LTI submission to the SIGMORPHON 2020 Shared Task 0: Language-Specific Cross-Lingual Transfer
This paper describes the CMU-LTI submission to the SIGMORPHON 2020 Shared Task 0 on typologically diverse morphological inflection.
SIGMORPHON 2020 Task 0 System Description: ETH Z\"urich Team
This paper presents our system for the SIGMORPHON 2020 Shared Task.
Exploring Neural Architectures And Techniques For Typologically Diverse Morphological Inflection
Morphological inflection in low resource languages is critical to augment existing corpora in Low Resource Languages, which can help develop several applications in these languages with very good social impact.
University of Illinois Submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
The objective of this shared task is to produce an inflected form of a word, given its lemma and a set of tags describing the attributes of the desired form.
Leveraging Principal Parts for Morphological Inflection
This paper presents the submission by the CU Ling team from the University of Colorado to SIGMORPHON 2020 shared task 0 on morphological inflection.
