Ireland

For decades, researchers in natural language processing and computational linguistics have been developing models and algorithms that aim to serve the needs of language documentation projects. However, these models have seen little use in language documentation despite their great potential for making documentary linguistic artefacts better and easier to produce. In this work, we argue that a major reason for this NLP gap is the lack of a strong foundation of application software which can on the one hand serve the complex needs of language documentation and on the other hand provide effortless integration with NLP models. We further present and describe a work-in-progress system we have developed to serve this need, Glam.


ACL 2022

Closing the NLP Gap Documentary Linguistics and NLP Need a Shared Software Infrastructure

language documentation

linguistics

low-resource

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
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The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

workshop paper

Machine translation for low-resource languages, such as Guarani, is a challenging task due to the lack of data. One way of tackling it is using pretrained word embeddings for model initialization. In this work we try to check if currently available data is enough to train rich embeddings for enhancing MT for Guarani and Spanish, by building a set of word embedding collections and training MT systems using them. We found that the trained vectors are strong enough to slightly improve the performance of some of the translation models and also to speed up the training convergence.

Can We Use Word Embeddings for Enhancing Guarani-Spanish Machine Translation?

we present a game with a purpose (GWAP) (Von Ahn, 2006). The aim of the game is to promote language learning and 'noticing' (Skehan, 2013). The game has been designed for Irish, but the framework could be used for other languages. Irish is a minority language which means that L2 learners have limited opportunities for exposure to the language, and additionally, there are also limited (digital) learning resources available. This research incorporates game development, language pedagogy and ICALL language materials development. This paper will focus on the language materials development as this is a bottleneck in the teaching and learning of minority and endangered languages.

Faoi Gheasa an adaptive game for Irish language learning

There are many challenges in morphological fieldwork annotation: it heavily relies on segmentation and feature labeling (which have both practical and theoretical drawbacks), it’s time-intensive, and the annotator needs to be linguistically trained and may still annotate things inconsistently. We propose a workflow that relies on unsupervised and active learning grounded in Word-and-Paradigm morphology
(WP). Machine learning has the potential to greatly accelerate the annotation process and allow a human annotator to focus on problematic cases, while the WP approach makes for an annotation system that is word-based and relational, removing the need to make decisions about feature labeling and segmentation early
in the process and allowing speakers of the language of interest to participate more actively, since linguistic training is not necessary. We present a proof-of-concept for the first step of the workflow: in a realistic fieldwork setting, annotators can process hundreds of forms per hour.

A Word-and-Paradigm Workflow for Fieldwork Annotation

This is a report on results obtained in the development of speech recognition tools intended to support linguistic documentation efforts. The test case is an extensive fieldwork corpus of Japhug, an endangered language of the Trans-Himalayan (Sino-Tibetan) family. The goal is to reduce the transcription workload of field linguists. The method used is a deep learning approach based on the language-specific tuning of a generic pre-trained representation model, XLS-R, using a Transformer architecture. We note difficulties in implementation, in terms of learning stability. But this approach brings significant improvements nonetheless. The quality of phonemic transcription is improved over earlier experiments; and most significantly, the new approach allows for reaching the stage of automatic word recognition. Subjective evaluation of the tool by the author of the training data confirms the usefulness of this approach.

Fine-tuning pre-trained models for Automatic Speech Recognition, experiments on a fieldwork corpus of Japhug (Trans-Himalayan family)

In Philotis (https://philotis.athenarc.gr/) we aim at supporting the researchers of living languages to develop annotated (linked) spoken and textual resources without external technical aid: ideally, speakers of the documented language and eager linguists alone would suffice. To this end, we take advantage of open-source NLP tools, semantic web technologies, annotation tools and universally adopted annotation and codification schemes. Pomak is our case study of endangered oral language. We make available existing and new textual and oral material of Pomak and develop annotated spoken and textual corpora. Here, we provide some information about Pomaks and their language and, briefly present our experience from the development of a Latin-based orthography and a morphologically annotated corpus of Pomak.

Morphologically annotated corpora of Pomak

This study investigates applications of automatic speech recognition (ASR) techniques to Hupa, a critically endangered Native American language from the Dene (Athabaskan) language family. Using around 9h12m of spoken data produced by one elder who is a first-language Hupa speaker, we experimented with different evaluation schemes and training settings. On average a fully connected deep neural network reached a word error rate of 35.26%. Our overall results illustrate the utility of ASR for making Hupa language documentation more accessible and usable. In addition, we found that when training acoustic models, using recordings with transcripts that were not carefully verified did not necessarily have a negative effect on model performance. This shows promise for speech corpora of indigenous languages that commonly include transcriptions produced by second-language speakers or linguists who have advanced knowledge in the language of interest.

Enhancing Documentation of Hupa with Automatic Speech Recognition | VIDEO

We present a benchmark in six European languages containing manually annotated information about olfactory situations and events following a FrameNet-like approach. The documents selection covers ten domains of interest to cultural historians in the olfactory do- main and includes texts published between 1620 to 1920, allowing a diachronic analysis of smell descriptions. With this work, we aim to foster the development of olfactory information extraction approaches as well as the analysis of changes in smell descriptions over time.

A Multilingual Benchmark to Capture Olfactory Situations over Time

Contextual word embedding techniques for semantic shift detection are receiving more and more attention. In this presentation, we present What is Done is Done (WiDiD), an incremental approach to semantic shift detection based on incremental clustering techniques and contextual embedding methods to capture the changes
over the meanings of a target word along a diachronic corpus. In WiDiD, the word contexts observed in the past are consolidated as a set of clusters that constitute the “memory” of the word meanings observed so far. Such a memory is exploited as a basis for subsequent word observations, so that the meanings observed in
the present are stratified over the past ones.

What is Done is Done: an Incremental Approach to Semantic Shift Detection

Language change has often been conceived as a competition between linguistic variants. However, language units may be complex organizations in themselves, e.g. in the case of schematic constructions, featuring a free slot. Such a slot is filled by words forming a set or 'paradigm' and engaging in inter-related dynamics within this constructional environment. To tackle this complexity, a simple computational method is offered to automatically characterize their interactions, and visualize them through networks of cooperation and competition. Applying this method to the French paradigm of quantifiers, I show that this method efficiently captures phenomena regarding the evolving organization of constructional paradigms, in particular the constitution of competing clusters of fillers that promote different semantic strategies overall.

From qualifiers to quantifiers: semantic shift at the paradigm level

Morphological and syntactic changes in word usage --- as captured, e.g., by grammatical profiles --- have been shown to be good predictors of a word's meaning change. In this work, we explore whether large pre-trained contextualised language models, a common tool for lexical semantic change detection, are sensitive to such morphosyntactic changes. To this end, we first compare the performance of grammatical profiles against that of a multilingual neural language model (XLM-R) on 10 datasets, covering 7 languages, and then combine the two approaches in ensembles to assess their complementarity. Our results show that ensembling grammatical profiles with XLM-R improves semantic change detection performance for most datasets and languages. This indicates that language models do not fully cover the fine-grained morphological and syntactic signals that are explicitly represented in grammatical profiles.


Closing the NLP Gap Documentary Linguistics and NLP Need a Shared Software Infrastructure

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Can We Use Word Embeddings for Enhancing Guarani-Spanish Machine Translation?

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The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error

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Closing the NLP Gap Documentary Linguistics and NLP Need a Shared Software Infrastructure

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Next from ACL 2022

Can We Use Word Embeddings for Enhancing Guarani-Spanish Machine Translation?

Similar lecture

The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads