pwc-archive/papers-with-abstracts huggingface dataset:
Challenges in assistive technology development for an endangered language: an Irish (Gaelic) perspective
Authors: Ailbhe Ni Chasaide, Emily Barnes, Neasa Ní Chiaráin, Ronan McGuirk, Oisín Morrin, Muireann Nic Corcráin, Julia Cummins
Date: 2022-05-01 00:00:00
Proceeding: SLPAT (ACL) 2022 5

This paper describes three areas of assistive technology development which deploy the resources and speech technology for Irish (Gaelic), newly emerging from the ABAIR initiative. These include (i) a screenreading facility for visually impaired people, (ii) an application to help develop phonological awareness and early literacy for dyslexic people (iii) a speech-enabled AAC system for non-speaking people. Each of these is at a different stage of development and poses unique challenges: these are dis-cussed along with the approaches adopted to address them. Three guiding principles underlie development. Firstly, the sociolinguistic context and the needs of the community are essential considerations in setting priorities. Secondly, development needs to be language sensitive. The need for skilled researchers with a deep knowledge of Irish structure is illustrated in the case of (ii) and (iii), where aspects of Irish linguistic structure (phonological, morphological and grammatical) and the striking differences from English pose challenges for systems aimed at bilingual Irish-English users. Thirdly, and most importantly, the users and their support networks are central – not as passive recipients of ready-made technologies, but as active partners at every stage of development, from design to implementation, evaluation and dissemination.
Cross-lingual Semantic Role Labelling with the ValPaL Database Knowledge
Authors: Chinmay Choudhary, Colm O’riordan
Date: 2022-05-01 00:00:00
Proceeding: DeeLIO (ACL) 2022 5
Tasks: Cross-Lingual Transfer, TAR, Transfer Learning

Cross-lingual Transfer Learning typically involves training a model on a high-resource sourcelanguage and applying it to a low-resource tar-get language. In this work we introduce a lexi-cal database calledValency Patterns Leipzig(ValPal)which provides the argument patterninformation about various verb-forms in mul-tiple languages including low-resource langua-ges. We also provide a framework to integratethe ValPal database knowledge into the state-of-the-art LSTM based model for cross-lingualsemantic role labelling. Experimental resultsshow that integrating such knowledge resultedin am improvement in performance of the mo-del on all the target languages on which it isevaluated.
How Do Transformer-Architecture Models Address Polysemy of Korean Adverbial Postpositions?
Authors: Seongmin Mun, Guillaume Desagulier
Date: 2022-05-01 00:00:00
Proceeding: DeeLIO (ACL) 2022 5
Tasks: Classification

Postpositions, which are characterized as multiple form-function associations and thus polysemous, pose a challenge to automatic identification of their usage. Several studies have used contextualized word-embedding models to reveal the functions of Korean postpositions. Despite the superior classification performance of previous studies, the particular reason how these models resolve the polysemy of Korean postpositions is not enough clear. To add more interpretation, for this reason, we devised a classification model by employing two transformer-architecture models—BERT and GPT-2—and introduces a computational simulation that interactively demonstrates how these transformer-architecture models simulate human interpretation of word-level polysemy involving Korean adverbial postpositions -ey, -eyse, and -(u)lo. Results reveal that (i) the BERT model performs better than the GPT-2 model to classify the intended function of postpositions, (ii) there is an inverse relationship between the classification accuracy and the number of functions that each postposition manifests, (iii) model performance is affected by the corpus size of each function, (iv) the models’ performance gradually improves as the epoch proceeds, and (vi) the models are affected by the scarcity of input and/or semantic closeness between the items.
Query Generation with External Knowledge for Dense Retrieval
Authors: Sukmin Cho, Soyeong Jeong, Wonsuk Yang, Jong Park
Date: 2022-05-01 00:00:00
Proceeding: DeeLIO (ACL) 2022 5
Tasks: Language Modeling, Language Modelling, Retrieval, Triplet

Dense retrieval aims at searching for the most relevant documents to the given query by encoding texts in the embedding space, requiring a large amount of query-document pairs to train. Since manually constructing such training data is challenging, recent work has proposed to generate synthetic queries from documents and use them to train a dense retriever. However, compared to the manually composed queries, synthetic queries do not generally ask for implicit information, therefore leading to a degraded retrieval performance. In this work, we propose Query Generation with External Knowledge (QGEK), a novel method for generating queries with external information related to the corresponding document. Specifically, we convert a query into a triplet-based template form to accommodate external information and transmit it to a pre-trained language model (PLM). We validate QGEK on both in-domain and out-domain dense retrieval settings. The dense retriever with the queries requiring implicit information is found to make good performance improvement. Also, such queries are similar to manually composed queries, confirmed by both human evaluation and unique & non-unique words distribution.
Uncovering Values: Detecting Latent Moral Content from Natural Language with Explainable and Non-Trained Methods
Authors: Luigi Asprino, Luana Bulla, Stefano De Giorgis, Aldo Gangemi, Ludovica Marinucci, Misael Mongiovi
Date: 2022-05-01 00:00:00
Proceeding: DeeLIO (ACL) 2022 5
Tasks: Knowledge Graphs, Natural Language Inference

Moral values as commonsense norms shape our everyday individual and community behavior. The possibility to extract moral attitude rapidly from natural language is an appealing perspective that would enable a deeper understanding of social interaction dynamics and the individual cognitive and behavioral dimension. In this work we focus on detecting moral content from natural language and we test our methods on a corpus of tweets previously labeled as containing moral values or violations, according to Moral Foundation Theory. We develop and compare two different approaches: (i) a frame-based symbolic value detector based on knowledge graphs and (ii) a zero-shot machine learning model fine-tuned on a task of Natural Language Inference (NLI) and a task of emotion detection. The final outcome from our work consists in two approaches meant to perform without the need for prior training process on a moral value detection task.
KIQA: Knowledge-Infused Question Answering Model for Financial Table-Text Data
Authors: Rungsiman Nararatwong, Natthawut Kertkeidkachorn, Ryutaro Ichise
Date: 2022-05-01 00:00:00
Proceeding: DeeLIO (ACL) 2022 5
Tasks: Entity Linking, Entity Retrieval, Language Modeling, Language Modelling, Question Answering, Retrieval

While entity retrieval models continue to advance their capabilities, our understanding of their wide-ranging applications is limited, especially in domain-specific settings. We highlighted this issue by using recent general-domain entity-linking models, LUKE and GENRE, to inject external knowledge into a question-answering (QA) model for a financial QA task with a hybrid tabular-textual dataset. We found that both models improved the baseline model by 1.57% overall and 8.86% on textual data. Nonetheless, the challenge remains as they still struggle to handle tabular inputs. We subsequently conducted a comprehensive attention-weight analysis, revealing how LUKE utilizes external knowledge supplied by GENRE. The analysis also elaborates how the injection of symbolic knowledge can be helpful and what needs further improvement, paving the way for future research on this challenging QA task and advancing our understanding of how a language model incorporates external knowledge.
Trans-KBLSTM: An External Knowledge Enhanced Transformer BiLSTM Model for Tabular Reasoning
Authors: Yerram Varun, Aayush Sharma, Vivek Gupta
Date: 2022-05-01 00:00:00
Proceeding: DeeLIO (ACL) 2022 5
Tasks: Common Sense Reasoning, Natural Language Inference

Natural language inference on tabular data is a challenging task. Existing approaches lack the world and common sense knowledge required to perform at a human level. While massive amounts of KG data exist, approaches to integrate them with deep learning models to enhance tabular reasoning are uncommon. In this paper, we investigate a new approach using BiLSTMs to incorporate knowledge effectively into language models. Through extensive analysis, we show that our proposed architecture, Trans-KBLSTM improves the benchmark performance on InfoTabS, a tabular NLI dataset.
On Masked Language Models for Contextual Link Prediction
Authors: Angus Brayne, Maciej Wiatrak, Dane Corneil
Date: 2022-05-01 00:00:00
Proceeding: DeeLIO (ACL) 2022 5
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction, Prediction

In the real world, many relational facts require context; for instance, a politician holds a given elected position only for a particular timespan. This context (the timespan) is typically ignored in knowledge graph link prediction tasks, or is leveraged by models designed specifically to make use of it (i.e. n-ary link prediction models). Here, we show that the task of n-ary link prediction is easily performed using language models, applied with a basic method for constructing cloze-style query sentences. We introduce a pre-training methodology based around an auxiliary entity-linked corpus that outperforms other popular pre-trained models like BERT, even with a smaller model. This methodology also enables n-ary link prediction without access to any n-ary training set, which can be invaluable in circumstances where expensive and time-consuming curation of n-ary knowledge graphs is not feasible. We achieve state-of-the-art performance on the primary n-ary link prediction dataset WD50K and on WikiPeople facts that include literals - typically ignored by knowledge graph embedding methods.
What Makes Good In-Context Examples for GPT-3?
Authors: Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, Weizhu Chen
Date: 2022-05-01 00:00:00
Proceeding: DeeLIO (ACL) 2022 5
Tasks: In-Context Learning, Natural Language Understanding, Open-Domain Question Answering, Question Answering, Retrieval, Sentence, Table-to-Text Generation, Text Generation

GPT-3 has attracted lots of attention due to its superior performance across a wide range of NLP tasks, especially with its in-context learning abilities. Despite its success, we found that the empirical results of GPT-3 depend heavily on the choice of in-context examples. In this work, we investigate whether there are more effective strategies for judiciously selecting in-context examples (relative to random sampling) that better leverage GPT-3’s in-context learning capabilities.Inspired by the recent success of leveraging a retrieval module to augment neural networks, we propose to retrieve examples that are semantically-similar to a test query sample to formulate its corresponding prompt. Intuitively, the examples selected with such a strategy may serve as more informative inputs to unleash GPT-3’s power of text generation. We evaluate the proposed approach on several natural language understanding and generation benchmarks, where the retrieval-based prompt selection approach consistently outperforms the random selection baseline. Moreover, it is observed that the sentence encoders fine-tuned on task-related datasets yield even more helpful retrieval results. Notably, significant gains are observed on tasks such as table-to-text generation (44.3% on the ToTTo dataset) and open-domain question answering (45.5% on the NQ dataset).
Development of the Siberian Ingrian Finnish Speech Corpus
Authors: Ivan Ubaleht, Taisto-Kalevi Raudalainen
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5

In this paper we present the speech corpus for the Siberian Ingrian Finnish language. The speech corpus includes audio data, annotations, software tools for data-processing, two databases and a web application. We have published part of the audio data and annotations. The software tool for parsing annotation files and feeding a relational database is developed and published under a free license. A web application is developed and available. At this moment, about 300 words and 200 phrases can be displayed using this web application.
New syntactic insights for automated Wolof Universal Dependency parsing
Authors: Bill Dyer
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5
Tasks: Dependency Parsing

Focus on language-specific properties with insights from formal minimalist syntax can improve universal dependency (UD) parsing. Such improvements are especially sensitive for low-resource African languages, like Wolof, which have fewer UD treebanks in number and amount of annotations, and fewer contributing annotators. For two different UD parser pipelines, one parser model was trained on the original Wolof treebank, and one was trained on an edited treebank. For each parser pipeline, the accuracy of the edited treebank was higher than the original for both the dependency relations and dependency labels. Accuracy for universal dependency relations improved as much as 2.90%, while accuracy for universal dependency labels increased as much as 3.38%. An annotation scheme that better fits a language’s distinct syntax results in better parsing accuracy.
Corpus Development of Kiswahili Speech Recognition Test and Evaluation sets, Preemptively Mitigating Demographic Bias Through Collaboration with Linguists
Authors: Kathleen Siminyu, Kibibi Mohamed Amran, Abdulrahman Ndegwa Karatu, Mnata Resani, Mwimbi Makobo Junior, Rebecca Ryakitimbo, Britone Mwasaru
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5
Tasks: Diversity, speech-recognition, Speech Recognition

Language technologies, particularly speech technologies, are becoming more pervasive for access to digital platforms and resources. This brings to the forefront concerns of their inclusivity, first in terms of language diversity. Additionally, research shows speech recognition to be more accurate for men than for women and more accurate for individuals younger than 30 years of age than those older. In the Global South where languages are low resource, these same issues should be taken into consideration in data collection efforts to not replicate these mistakes. It is also important to note that in varying contexts within the Global South, this work presents additional nuance and potential for bias based on accents, related dialects and variants of a language. This paper documents i) the designing and execution of a Linguists Engagement for purposes of building an inclusive Kiswahili Speech Recognition dataset, representative of the diversity among speakers of the language ii) the unexpected yet key learning in terms of socio-linguistcs which demonstrate the importance of multi-disciplinarity in teams developing datasets and NLP technologies iii) the creation of a test dataset intended to be used for evaluating the performance of Speech Recognition models on demographic groups that are likely to be underrepresented.
CLD² Language Documentation Meets Natural Language Processing for Revitalising Endangered Languages
Authors: Roberto Zariquiey, Arturo Oncevay, Javier Vera
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5

Language revitalisation should not be understood as a direct outcome of language documentation, which is mainly focused on the creation of language repositories. Natural language processing (NLP) offers the potential to complement and exploit these repositories through the development of language technologies that may contribute to improving the vitality status of endangered languages. In this paper, we discuss the current state of the interaction between language documentation and computational linguistics, present a diagnosis of how the outputs of recent documentation projects for endangered languages are underutilised for the NLP community, and discuss how the situation could change from both the documentary linguistics and NLP perspectives. All this is introduced as a bridging paradigm dubbed as Computational Language Documentation and Development (CLD²). CLD² calls for (1) the inclusion of NLP-friendly annotated data as a deliverable of future language documentation projects; and (2) the exploitation of language documentation databases by the NLP community to promote the computerization of endangered languages, as one way to contribute to their revitalization.
One Wug, Two Wug+s Transformer Inflection Models Hallucinate Affixes
Authors: Farhan Samir, Miikka Silfverberg
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5
Tasks: Data Augmentation, Hallucination, Inductive Bias, Morphological Inflection, Vocal Bursts Valence Prediction

Data augmentation strategies are increasingly important in NLP pipelines for low-resourced and endangered languages, and in neural morphological inflection, augmentation by so called data hallucination is a popular technique. This paper presents a detailed analysis of inflection models trained with and without data hallucination for the low-resourced Canadian Indigenous language Gitksan. Our analysis reveals evidence for a concatenative inductive bias in augmented models—in contrast to models trained without hallucination, they strongly prefer affixing inflection patterns over suppletive ones. We find that preference for affixation in general improves inflection performance in “wug test” like settings, where the model is asked to inflect lexemes missing from the training set. However, data hallucination dramatically reduces prediction accuracy for reduplicative forms due to a misanalysis of reduplication as affixation. While the overall impact of data hallucination for unseen lexemes remains positive, our findings call for greater qualitative analysis and more varied evaluation conditions in testing automatic inflection systems. Our results indicate that further innovations in data augmentation for computational morphology are desirable.
G_i2P_i Rule-based, index-preserving grapheme-to-phoneme transformations
Authors: Aidan Pine, Patrick William Littell, Eric Joanis, David Huggins-Daines, Christopher Cox, Fineen Davis, Eddie Antonio Santos, Shankhalika Srikanth, Delasie Torkornoo, Sabrina Yu
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5

This paper describes the motivation and implementation details for a rule-based, index-preserving grapheme-to-phoneme engine ‘G_i2P_i' implemented in pure Python and released under the open source MIT license. The engine and interface have been designed to prioritize the developer experience of potential contributors without requiring a high level of programming knowledge. ‘G_i2P_i' already provides mappings for 30 (mostly Indigenous) languages, and the package is accompanied by a web-based interactive development environment, a RESTful API, and extensive documentation to encourage the addition of more mappings in the future. We also present three downstream applications of ‘G_i2P_i' and show results of a preliminary evaluation.
Shallow Parsing for Nepal Bhasa Complement Clauses
Authors: Borui Zhang, Abe Kazemzadeh, Brian Reese
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5
Tasks: Chunking, Transfer Learning

Accelerating the process of data collection, annotation, and analysis is an urgent need for linguistic fieldwork and documentation of endangered languages (Bird, 2009). Our experiments describe how we maximize the quality for the Nepal Bhasa syntactic complement structure chunking model. Native speaker language consultants were trained to annotate a minimally selected raw data set (Suárez et al.,2019). The embedded clauses, matrix verbs, and embedded verbs are annotated. We apply both statistical training algorithms and transfer learning in our training, including Naive Bayes, MaxEnt, and fine-tuning the pre-trained mBERT model (Devlin et al., 2018). We show that with limited annotated data, the model is already sufficient for the task. The modeling resources we used are largely available for many other endangered languages. The practice is easy to duplicate for training a shallow parser for other endangered languages in general.
Using LARA to create image-based and phonetically annotated multimodal texts for endangered languages
Authors: Branislav Bédi, Hakeem Beedar, Belinda Chiera, Nedelina Ivanova, Christèle Maizonniaux, Neasa Ní Chiaráin, Manny Rayner, John Sloan, Ghil’Ad Zuckermann
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5
Tasks: Relation

We describe recent extensions to the open source Learning And Reading Assistant (LARA) supporting image-based and phonetically annotated texts. We motivate the utility of these extensions both in general and specifically in relation to endangered and archaic languages, and illustrate with examples from the revived Australian language Barngarla, Icelandic Sign Language, Irish Gaelic, Old Norse manuscripts and Egyptian hieroglyphics.
Recovering Text from Endangered Languages Corrupted PDF documents
Authors: Nicolas Stefanovitch
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5

In this paper we present an approach to efficiently recover texts from corrupted documents of endangered languages. Textual resources for such languages are scarce, and sometimes the few available resources are corrupted PDF documents. Endangered languages are not supported by standard tools and present even the additional difficulties of not possessing any corpus over which to train language models to assist with the recovery. The approach presented is able to fully recover born digital PDF documents with minimal effort, thereby helping the preservation effort of endangered languages, by extending the range of documents usable for corpus building.
Learning Through Transcription
Authors: Mat Bettinson, Steven Bird
Date: 2022-05-01 00:00:00
Proceeding: ComputEL (ACL) 2022 5
Tasks: Language Acquisition

Transcribing speech for primarily oral, local languages is often a joint effort involving speakers and outsiders. It is commonly motivated by externally-defined scientific goals, alongside local motivations such as language acquisition and access to heritage materials. We explore the task of ‘learning through transcription’ through the design of a system for collaborative speech annotation. We have developed a prototype to support local and remote learner-speaker interactions in remote Aboriginal communities in northern Australia. We show that situated systems design for inclusive non-expert practice is a promising new direction for working with speakers of local languages.