pwc-archive/papers-with-abstracts huggingface dataset:
Ensemble Self-Training for Low-Resource Languages: Grapheme-to-Phoneme Conversion and Morphological Inflection
Authors: Xiang Yu, Ngoc Thang Vu, Jonas Kuhn
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Data Augmentation, Grapheme-to-Phoneme Conversion, 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. We apply this framework on two SIGMORPHON 2020 shared tasks: grapheme-to-phoneme conversion and morphological inflection. With very simple base models in the ensemble, we rank the first and the fourth in these two tasks. We show in the analysis that our system works especially well on low-resource languages.
The CMU-LTI submission to the SIGMORPHON 2020 Shared Task 0: Language-Specific Cross-Lingual Transfer
Authors: Nikitha Murikinati, Antonios Anastasopoulos
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Cross-Lingual Transfer, Morphological Inflection

This paper describes the CMU-LTI submission to the SIGMORPHON 2020 Shared Task 0 on typologically diverse morphological inflection. The (unrestricted) submission uses the cross-lingual approach of our last year{'}s winning submission (Anastasopoulos and Neubig, 2019), but adapted to use specific transfer languages for each test language. Our system, with fixed non-tuned hyperparameters, achieved a macro-averaged accuracy of 80.65 ranking 20th among 31 systems, but it was still tied for best system in 25 of the 90 total languages.
Grapheme-to-Phoneme Conversion with a Multilingual Transformer Model
Authors: Omnia ElSaadany, Benjamin Suter
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Grapheme-to-Phoneme Conversion

In this paper, we describe our three submissions to the SIGMORPHON 2020 shared task 1 on grapheme-to-phoneme conversion for 15 languages. We experimented with a single multilingual transformer model. We observed that the multilingual model achieves results on par with our separately trained monolingual models and is even able to avoid a few of the errors made by the monolingual models.
The IMS--CUBoulder System for the SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion
Authors: Manuel Mager, Katharina Kann
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Task 2

In this paper, we present the systems of the University of Stuttgart IMS and the University of Colorado Boulder (IMS--CUBoulder) for SIGMORPHON 2020 Task 2 on unsupervised morphological paradigm completion (Kann et al., 2020). The task consists of generating the morphological paradigms of a set of lemmas, given only the lemmas themselves and unlabeled text. Our proposed system is a modified version of the baseline introduced together with the task. In particular, we experiment with substituting the inflection generation component with an LSTM sequence-to-sequence model and an LSTM pointer-generator network. Our pointer-generator system obtains the best score of all seven submitted systems on average over all languages, and outperforms the official baseline, which was best overall, on Bulgarian and Kannada.
SIGMORPHON 2020 Task 0 System Description: ETH Z\"urich Team
Authors: Martina Forster, Clara Meister
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Machine Translation, Morphological Inflection, NMT, Translation

This paper presents our system for the SIGMORPHON 2020 Shared Task. We build off of the baseline systems, performing exact inference on models trained on language family data. Our systems return the globally best solution under these models. Our two systems achieve 80.9{\%} and 75.6{\%} accuracy on the test set. We ultimately find that, in this setting, exact inference does not seem to help or hinder the performance of morphological inflection generators, which stands in contrast to its affect on Neural Machine Translation (NMT) models.
KU-CST at the SIGMORPHON 2020 Task 2 on Unsupervised Morphological Paradigm Completion
Authors: Manex Agirrezabal, J{\"u}rgen Wedekind
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: LEMMA, Task 2

We present a model for the unsupervised dis- covery of morphological paradigms. The goal of this model is to induce morphological paradigms from the bible (raw text) and a list of lemmas. We have created a model that splits each lemma in a stem and a suffix, and then we try to create a plausible suffix list by con- sidering lemma pairs. Our model was not able to outperform the official baseline, and there is still room for improvement, but we believe that the ideas presented here are worth considering.
Low-Resource G2P and P2G Conversion with Synthetic Training Data
Authors: Bradley Hauer, Amir Ahmad Habibi, Yixing Luan, Arnob Mallik, Grzegorz Kondrak
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Grapheme-to-Phoneme Conversion

This paper presents the University of Alberta systems and results in the SIGMORPHON 2020 Task 1: Multilingual Grapheme-to-Phoneme Conversion. Following previous SIGMORPHON shared tasks, we define a low-resource setting with 100 training instances. We experiment with three transduction approaches in both standard and low-resource settings, as well as on the related task of phoneme-to-grapheme conversion. We propose a method for synthesizing training data using a combination of diverse models.
Frustratingly Easy Multilingual Grapheme-to-Phoneme Conversion
Authors: Nikhil Prabhu, Katharina Kann
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Grapheme-to-Phoneme Conversion

In this paper, we describe two CU-Boulder submissions to the SIGMORPHON 2020 Task 1 on multilingual grapheme-to-phoneme conversion (G2P). Inspired by the high performance of a standard transformer model (Vaswani et al., 2017) on the task, we improve over this approach by adding two modifications: (i) Instead of training exclusively on G2P, we additionally create examples for the opposite direction, phoneme-to-grapheme conversion (P2G). We then perform multi-task training on both tasks. (ii) We produce ensembles of our models via majority voting. Our approaches, though being conceptually simple, result in systems that place 6th and 8th amongst 23 submitted systems, and obtain the best results out of all systems on Lithuanian and Modern Greek, respectively.
Exploring Neural Architectures And Techniques For Typologically Diverse Morphological Inflection
Authors: Pratik Jayarao, Siddhanth Pillay, Pranav Thombre, Aditi Chaudhary
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Decoder, Hallucination, 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. We describe our attention-based encoder-decoder approach that we implement using LSTMs and Transformers as the base units. We also describe the ancillary techniques that we experimented with, such as hallucination, language vector injection, sparsemax loss and adversarial language network alongside our approach to select the related language(s) for training. We present the results we generated on the constrained as well as unconstrained SIGMORPHON 2020 dataset (CITATION). One of the primary goals of our paper was to study the contribution varied components described above towards the performance of our system and perform an analysis on the same.
University of Illinois Submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
Authors: Marc Canby, Aidana Karipbayeva, Bryan Lunt, Sah Mozaffari, Charlotte Yoder, Julia Hockenmaier
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Decoder, Form, LEMMA, 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. In this paper, we describe a transformer-based model that uses a bidirectional decoder to perform this task, and evaluate its performance on the 90 languages and 18 language families used in this task.
Leveraging Principal Parts for Morphological Inflection
Authors: Ling Liu, Mans Hulden
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Form, LEMMA, 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. The task is to generate the target inflected word form given a lemma form and a target morphosyntactic description. Our system uses the Transformer architecture. Our overall approach is to treat the morphological inflection task as a paradigm cell filling problem and to design the system to leverage principal parts information for better morphological inflection when the training data is limited. We train one model for each language separately without external data. The overall average performance of our submission ranks the first in both average accuracy and Levenshtein distance from the gold inflection among all submissions including those using external resources.
Linguist vs. Machine: Rapid Development of Finite-State Morphological Grammars
Authors: Sarah Beemer, Zak Boston, April Bukoski, Daniel Chen, Princess Dickens, Andrew Gerlach, Torin Hopkins, an, Parth Jawale, Chris Koski, Akanksha Malhotra, Piyush Mishra, Saliha Muradoglu, Lan Sang, Tyler Short, Sagarika Shreevastava, Elizabeth Spaulding, Testumichi Umada, Beilei Xiang, Changbing Yang, Mans Hulden
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Morphological Inflection

Sequence-to-sequence models have proven to be highly successful in learning morphological inflection from examples as the series of SIGMORPHON/CoNLL shared tasks have shown. It is usually assumed, however, that a linguist working with inflectional examples could in principle develop a gold standard-level morphological analyzer and generator that would surpass a trained neural network model in accuracy of predictions, but that it may require significant amounts of human labor. In this paper, we discuss an experiment where a group of people with some linguistic training develop 25+ grammars as part of the shared task and weigh the cost/benefit ratio of developing grammars by hand. We also present tools that can help linguists triage difficult complex morphophonological phenomena within a language and hypothesize inflectional class membership. We conclude that a significant development effort by trained linguists to analyze and model morphophonological patterns are required in order to surpass the accuracy of neural models.
CLUZH at SIGMORPHON 2020 Shared Task on Multilingual Grapheme-to-Phoneme Conversion
Authors: Peter Makarov, Simon Clematide
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Grapheme-to-Phoneme Conversion, Imitation Learning

This paper describes the submission by the team from the Institute of Computational Linguistics, Zurich University, to the Multilingual Grapheme-to-Phoneme Conversion (G2P) Task of the SIGMORPHON 2020 challenge. The submission adapts our system from the 2018 edition of the SIGMORPHON shared task. Our system is a neural transducer that operates over explicit edit actions and is trained with imitation learning. It is well-suited for morphological string transduction partly because it exploits the fact that the input and output character alphabets overlap. The challenge posed by G2P has been to adapt the model and the training procedure to work with disjoint alphabets. We adapt the model to use substitution edits and train it with a weighted finite-state transducer acting as the expert policy. An ensemble of such models produces competitive results on G2P. Our submission ranks second out of 23 submissions by a total of nine teams.
The UniMelb Submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
Authors: Andreas Scherbakov
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Hallucination, Morphological Inflection

The paper describes the University of Melbourne{'}s submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection. Our team submitted three systems in total, two neural and one non-neural. Our analysis of systems{'} performance shows positive effects of newly introduced data hallucination technique that we employed in one of neural systems, especially in low-resource scenarios. A non-neural system based on observed inflection patterns shows optimistic results even in its simple implementation ({\textgreater}75{\%} accuracy for 50{\%} of languages). With possible improvement within the same modeling principle, accuracy might grow to values above 90{\%}.
Data Augmentation for Transformer-based G2P
Authors: Zach Ryan, Mans Hulden
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Data Augmentation

The Transformer model has been shown to outperform other neural seq2seq models in several character-level tasks. It is unclear, however, if the Transformer would benefit as much as other seq2seq models from data augmentation strategies in the low-resource setting. In this paper we explore strategies for data augmentation in the g2p task together with the Transformer model. Our results show that a relatively simple alignment-based strategy of identifying consistent input-output subsequences in grapheme-phoneme data coupled together with a subsequent splicing together of such pieces to generate hallucinated data works well in the low-resource setting, often delivering substantial performance improvement over a standard Transformer model.
Transliteration for Cross-Lingual Morphological Inflection
Authors: Nikitha Murikinati, Antonios Anastasopoulos, Graham Neubig
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Cross-Lingual Transfer, Grapheme-to-Phoneme Conversion, Morphological Inflection, Transliteration

Cross-lingual transfer between typologically related languages has been proven successful for the task of morphological inflection. However, if the languages do not share the same script, current methods yield more modest improvements. We explore the use of transliteration between related languages, as well as grapheme-to-phoneme conversion, as data preprocessing methods in order to alleviate this issue. We experimented with several diverse language pairs, finding that in most cases transliterating the transfer language data into the target one leads to accuracy improvements, even up to 9 percentage points. Converting both languages into a shared space like the International Phonetic Alphabet or the Latin alphabet is also beneficial, leading to improvements of up to 16 percentage points.
Getting the \#\#life out of living: How Adequate Are Word-Pieces for Modelling Complex Morphology?
Authors: Stav Klein, Reut Tsarfaty
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: TAG, Word Embeddings

This work investigates the most basic units that underlie contextualized word embeddings, such as BERT {---} the so-called word pieces. In Morphologically-Rich Languages (MRLs) which exhibit morphological fusion and non-concatenative morphology, the different units of meaning within a word may be fused, intertwined, and cannot be separated linearly. Therefore, when using word-pieces in MRLs, we must consider that: (1) a linear segmentation into sub-word units might not capture the full morphological complexity of words; and (2) representations that leave morphological knowledge on sub-word units inaccessible might negatively affect performance. Here we empirically examine the capacity of word-pieces to capture morphology by investigating the task of multi-tagging in Modern Hebrew, as a proxy to evaluate the underlying segmentation. Our results show that, while models trained to predict multi-tags for complete words outperform models tuned to predict the distinct tags of WPs, we can improve the WPs tag prediction by purposefully constraining the word-pieces to reflect their internal functions. We suggest that linguistically-informed word-pieces schemes, that make the morphological structure explicit, might boost performance for MRLs.
Joint learning of constraint weights and gradient inputs in Gradient Symbolic Computation with constrained optimization
Authors: Max Nelson
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

This paper proposes a method for the joint optimization of constraint weights and symbol activations within the Gradient Symbolic Computation (GSC) framework. The set of grammars representable in GSC is proven to be a subset of those representable with lexically-scaled faithfulness constraints. This fact is then used to recast the problem of learning constraint weights and symbol activations in GSC as a quadratically-constrained version of learning lexically-scaled faithfulness grammars. This results in an optimization problem that can be solved using Sequential Quadratic Programming.
In search of isoglosses: continuous and discrete language embeddings in Slavic historical phonology
Authors: Chundra Cathcart, W, Florian l
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

This paper investigates the ability of neural network architectures to effectively learn diachronic phonological generalizations in amultilingual setting. We employ models using three different types of language embedding (dense, sigmoid, and straight-through). We find that the Straight-Through model out-performs the other two in terms of accuracy, but the Sigmoid model{'}s language embeddings show the strongest agreement with the traditional subgrouping of the Slavic languages. We find that the Straight-Through model has learned coherent, semi-interpretable information about sound change, and outline directions for future research.
Multi-Tiered Strictly Local Functions
Authors: Phillip Burness, Kevin McMullin
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

Tier-based Strictly Local functions, as they have so far been defined, are equipped with just a single tier. In light of this fact, they are currently incapable of modelling simultaneous phonological processes that would require different tiers. In this paper we consider whether and how we can allow a single function to operate over more than one tier. We conclude that multiple tiers can and should be permitted, but that the relationships between them must be restricted in some way to avoid overgeneration. The particular restriction that we propose comes in two parts. First, each input element is associated with a set of tiers that on their own can fully determine what the element is mapped to. Second, the set of tiers associated to a given input element must form a strict superset-subset hierarchy. In this way, we can track multiple, related sources of information when deciding how to process a particular input element. We demonstrate that doing so enables simple and intuitive analyses to otherwise challenging phonological phenomena.
Corpus based Amharic sentiment lexicon generation
Authors: Girma Neshir Alemneh, Andreas Rauber, Solomon Atnafu
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: General Classification, Sentiment Analysis, Sentiment Classification

Sentiment classification is an active research area with several applications including analysis of political opinions, classifying comments, movie reviews, news reviews and product reviews. To employ rule based sentiment classification, we require sentiment lexicons. However, manual construction of sentiment lexicon is time consuming and costly for resource-limited languages. To bypass manual development time and costs, we tried to build Amharic Sentiment Lexicons relying on corpus based approach. The intention of this approach is to handle sentiment terms specific to Amharic language from Amharic Corpus. Small set of seed terms are manually prepared from three parts of speech such as noun, adjective and verb. We developed algorithms for constructing Amharic sentiment lexicons automatically from Amharic news corpus. Corpus based approach is proposed relying on the word co-occurrence distributional embedding including frequency based embedding (i.e. Positive Point-wise Mutual Information PPMI). Using PPMI with threshold value of 100 and 200, we got corpus based Amharic Sentiment lexicons of size 1811 and 3794 respectively by expanding 519 seeds. Finally, the lexicon generated in corpus based approach is evaluated.
Negation handling for Amharic sentiment classification
Authors: Girma Neshir Alemneh, Andreas Rauber, Solomon Atnafu
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7
Tasks: Classification, General Classification, Negation, Sentiment Analysis, Sentiment Classification

User generated content is bringing new aspects of processing data on the web. Due to the advancement of World Wide Web technology, users are not only consumer of web contents but also they are producers of contents in the form of text, audio, video and picture. This study focuses on the analysis of textual contents with subjective information (referring to sentiment analysis). Most of conventional approaches of sentiment analysis do not effectively capture negation in languages where there are limited computational linguistic resources (e.g. Amharic). For this research, we proposed Amharic negation handling framework for Amharic sentiment classification. The proposed framework combines the lexicon based sentiment classification approach and character ngram based machine learning algorithms. Finally, the performance of framework is evaluated using the annotated Amharic news comments. The system is performing the best of all models and the baselines with accuracy of 98.0. The result is compared with the baselines (without negation handling and word level ngram model).
Embedding Oriented Adaptable Semantic Annotation Framework for Amharic Web Documents
Authors: Kidane Woldemariyam, Dr. Fekade Getahun
Date: 2020-07-01 00:00:00
Proceeding: WS 2020 7

The Web has become a source of information, where information is provided by humans for humans and its growth has increased necessity to get solutions that intelligently extract valuable knowledge from existing and newly added web documents with no (minimal) supervisions. However, due to the unstructured nature of existing data on the Web, effective extraction of this knowledge is limited for both human beings and software agents. Thus, this research work designed generic and embedding oriented framework that automatically annotates semantically Amharic web documents using ontology. This framework significantly reduces manual annotation and learning cost used for semantic annotation of Amharic web documents with its nature of adaptability with minimal modification. The results have also implied that neural network techniques are promising for semantic annotation, especially for less resourced languages like Amharic in comparison to language dependent techniques that have cost of speed and challenge of adaptation into new domains and languages. We experiment the feasibility of the proposed approach using Amharic news collected from WALTA news agency and Amharic Wikipedia. Our results show that the proposed solution exhibits 70.68{\%} of precision, 66.89{\%} of recall and 68.53{\%} of f-measure in semantic annotation for a morphologically complex Amharic language with limited size dataset.