Ireland

This work presents our contribution from the Text-to-Knowledge team of Ghent University (UGent-T2K) to the MultiDoc2Dial shared task on modeling dialogs grounded in multiple documents. We propose a pipeline system, comprising (1) document retrieval, (2) passage retrieval, and (3) response generation. We engineered these individual components mainly by, for (1)-(2), combining multiple ranking models and adding a final LambdaMART reranker, and, for (3), by adopting a Fusion-in-Decoder (FiD) model. We thus significantly boost the baseline system’s performance (over +10 points for both F1 and SacreBLEU). Further, error analysis reveals two major failure cases, to be addressed in future work: (i) in case of topic shift within the dialog, retrieval often fails to select the correct grounding document(s), and (ii) generation sometimes fails to use the correctly retrieved grounding passage.

ACL 2022

UGent-T2K at the 2nd DialDoc Shared Task: A Retrieval-Focused Dialog System Grounded in Multiple Documents

knowledge-grounded dialog generation

conversational information retrieval

dialog system

## Welcome to EMNLP 2022!
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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

workshop paper

# 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. 
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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). 

MultiDoc2Dial presents an important challenge on modeling dialogues grounded with multiple documents. This paper proposes a pipeline system of "retrieve, re-rank, and generate", where each component is individually optimized. This enables the passage re-ranker and response generator to fully exploit training with ground-truth data. Furthermore, we use a deep cross-encoder trained with localized hard negative passages from the retriever. For the response generator, we use grounding span prediction as an auxiliary task to be jointly trained with the main task of response generation. We also adopt a passage dropout and regularization technique to improve response generation performance. Experimental results indicate that the system clearly surpasses the competitive baseline and our team CPII-NLP ranked 1st on the seen test and 2nd on the unseen test of this challenge.

Grounded Dialogue Generation with Cross-encoding Re-ranker, Grounding Span Prediction, and Passage Dropout

This presentation discusses our proposed approach, Docalog, for the DialDoc-22 (Multi-Doc2Dial) shared task. Docalog identifies themost relevant knowledge in the associated document, in a multi-document setting. Docalog, is a three-stage pipeline consisting of (1) a document retriever model (DR. TEIT), (2) an answer span prediction model, and (3) an ultimate span picker deciding on the most likely answer span, out of all predicted spans. In the test phase of MultiDoc2Dial 2022, Docalog achieved f1-scores of 36.07% and 28.44% and SacreBLEU scores of 23.70% and 20.52%, respectively on the MDD-SEEN and MDD-UNSEEN folds.

Docalog: Multi-document Dialogue System using Transformer-based Span Retrieval

In this presentation we describe 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.

Development of the Siberian Ingrian Finnish Speech Corpus

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 con- tributing 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 de- pendency relations and dependency labels. Ac- curacy for universal dependency relations im- proved 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.

New syntactic insights for automated Wolof Universal Dependency parsing

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.

Corpus Development of Kiswahili Speech Recognition Test and Evaluation sets, Preemptively Mitigating Demographic Bias Through Collaboration with Linguists

Many archival recordings of speech from endangered languages remain unannotated and inaccessible to community members and language learning programs. One bottleneck is the time-intensive nature of annotation. An even narrower bottleneck occurs for recordings with access constraints, such as language that must be vetted or filtered by authorised community members before annotation can begin. We propose a privacy-preserving workflow to widen both bottlenecks for recordings where speech in the endangered language is intermixed with a more widely-used language such as English for meta-linguistic commentary and questions (e.g. What is the word for 'tree'?). We integrate voice activity detection (VAD), spoken language identification (SLI), and automatic speech recognition (ASR) to transcribe the metalinguistic content, which an authorised person can quickly scan to triage recordings that can be annotated by people with lower levels of access. We report work-in-progress processing 136 hours archival audio containing a mix of English and Muruwari. Our collaborative work with the Muruwari custodian of the archival materials show that this workflow reduces metalanguage transcription time by 20% even given only minimal amounts of annotated training data: 10 utterances per language for SLI and for ASR at most 39 minutes, and possibly as little as 39 seconds.

Automated speech tools for helping communities process restricted-access corpora for language revival efforts

This paper describes the motivation and implementation details for a rule-based, index-preserving grapheme-to-phoneme engine Gᵢ2Pᵢ 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. Gᵢ2Pᵢ 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 Gᵢ2Pᵢ and show results of a preliminary evaluation.

Gi2Pi Rule-based, index-preserving grapheme-to-phoneme transformations

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

Recovering Text from Endangered Languages Corrupted PDF documents

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.


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

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.

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Grounded Dialogue Generation with Cross-encoding Re-ranker, Grounding Span Prediction, and Passage Dropout

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Grounded Dialogue Generation with Cross-encoding Re-ranker, Grounding Span Prediction, and Passage Dropout

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Controllable Factuality in Document-Grounded Dialog Systems Using a Noisy Channel Model

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