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Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study 4

Automating Sound Change Prediction for Phylogenetic Inferenc...

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4th International Workshop on Computational Approaches to Historical language Change, LChange 2023

作者： Chang, Kalvin Robinson, Nathaniel R. Cai, Anna Chen, Ting Zhang, Annie Mortensen, David R. School of Computer Science Carnegie Mellon University United States Center for Language and Speech Processing Johns Hopkins University United States

ISBN: (纸本)9798891760431

We describe a set of new methods to partially automate linguistic phylogenetic inference given (1) cognate sets with their respective protoforms and sound laws, (2) a mapping from phones to their articulatory features and (3) a typological database of sound changes. We train a neural network on these sound change data to weight articulatory distances between phones and predict intermediate sound change steps between historical protoforms and their modern descendants, replacing a linguistic expert in part of a parsimony-based phylogenetic inference algorithm. In our best experiments on Tukanoan languages, this method produces trees with a Generalized Quartet Distance of 0.12 from a tree that used expert annotations, a significant improvement over other semi-automated baselines. We discuss potential benefits and drawbacks to our neural approach and parsimony-based tree prediction. We also experiment with a minimal generalization learner for automatic sound law induction, finding it less effective than sound laws from expert annotation. Our code is publicly available. © 2023 Association for Computational Linguistics.

关键词： Inference engines

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MaiBaam: A Multi-Dialectal Bavarian Universal Dependency Treebank 30

MaiBaam: A Multi-Dialectal Bavarian Universal Dependency Tre...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on language Resources and Evaluation, LREC-COLING 2024

作者： Blaschke, Verena Kovačić, Barbara Peng, Siyao Schütze, Hinrich Plank, Barbara Center for Information and Language Processing LMU Munich Germany Munich Germany Department of Computer Science IT University of Copenhagen Denmark

ISBN: (纸本)9782493814104

Despite the success of the Universal Dependencies (UD) project exemplified by its impressive language breadth, there is still a lack in 'within-language breadth': most treebanks focus on standard languages. Even for German, the language with the most annotations in UD, so far no treebank exists for one of its language varieties spoken by over 10M people: Bavarian. To contribute to closing this gap, we present the first multi-dialect Bavarian treebank (MaiBaam) manually annotated with part-of-speech and syntactic dependency information in UD, covering multiple text genres (wiki, fiction, grammar examples, social, non-fiction). We highlight the morphosyntactic differences between the closely-related Bavarian and German and showcase the rich variability of speakers' orthographies. Our corpus includes 15k tokens, covering dialects from all Bavarian-speaking areas spanning three countries. We provide baseline parsing and POS tagging results, which are lower than results obtained on German and vary substantially between different graph-based parsers. To support further research on Bavarian syntax, we make our dataset, language-specific guidelines and code publicly available. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Syntactics

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Data management in Ethereum DApps: A cost and performance analysis

Data management in Ethereum DApps: A cost and performance an...

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作者： Kostamis, Periklis Sendros, Andreas Efraimidis, Pavlos S. Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi67100 Greece Institute for Language and Speech Processing Athena Research Center Xanthi67100 Greece

The cost of interacting with a blockchain and the time required to search and retrieve information from it are important factors in the design of Decentralized Applications (DApps). The corresponding approaches for data management in Ethereum vary from pure on-chain solutions to hybrid architectures. In this work, we thoroughly examine a wide range of data management methods, including unconventional ones, and evaluate them in terms of storage cost and retrieval latency while considering the impact of recent hard forks. More precisely, we elaborate on the use of Smart Contract (SC) storage as a data store for DApps and in view of the prohibitive cost inherent to this approach, we present and compare methods for low-cost data handling in Ethereum, namely the event-logs, the transaction payload, and the almost surprising exploitation of unused function arguments. In addition, we study hybrid schemes by using IPFS and Swarm as storage platforms and Ethereum as a timestamping proof mechanism. Such schemes are especially effective when large chunks of data have to be managed. The upload and retrieval performance of these platforms and the cost associated with storing (on-chain) the identifiers of the uploaded data are of utmost importance and therefore are investigated in detail. © 2023

关键词： Smart contract

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Exploring language-Agnostic speech Representations Using Domain Knowledge for Detecting Alzheimer's Dementia 48

Exploring Language-Agnostic Speech Representations Using Dom...

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48th IEEE International Conference on Acoustics, speech and Signal processing, ICASSP 2023

作者： Shah, Zehra Qi, Shi-Ang Wang, Fei Farrokh, Mahtab Tasnim, Mashrura Stroulia, Eleni Greiner, Russell Plitsis, Manos Katsamanis, Athanasios University of Alberta Department of Computing Science Edmonton Canada Athena Research Center Institute for Language and Speech Processing Greece

ISBN: (纸本)9781728163277

We explore ways to use speech data to screen for indications of Alzheimer's dementia (AD). In particular, we describe our approach to the ICASSP 2023 Signal processing Grand Challenge, which involves extrapolating from models learned from English speech samples, to Greek speech samples, to determine which subjects have AD. By using acoustic and linguistic features, inspired by clinical research on AD, our top-performing classification model achieves 69% accuracy in distinguishing AD patients from healthy controls, and our regression model attains an RMSE of 4.8 for inferring cognitive testing scores. These outcomes underscore the potential of our explainable model for detecting cognitive decline in AD patients via speech, and its applicability in clinical settings. © 2023 IEEE.

关键词： Alzheimer's dementia Machine Learning Signal processing speech

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Improved G723.1 Codec speech Quality Under Burst Packet Loss Conditions 5th

Improved G723.1 Codec Speech Quality Under Burst Packet Loss...

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5th International Conference on Electrical Engineering and Control Applications, ICEECA 2022

作者： Bakri, Adil Mahdjane, Karima Amrouche, Abderrahmane Krobba, Ahmed Scientific Research and Technical Center for the Development of Arabic Language CRSTDLA Algiers Algeria Speech Communication and Signal Processing Laboratory Faculty of Electronics and Computer Science USTHB Algiers Algeria

ISBN: (纸本)9789819747757

In this paper, a Packet Loss Concealment (PLC) algorithm is proposed for G723.1 CELP-type speech coders in order to improve the quality of decoded speech in VoIP under burst packet loss. The original PLC method implemented in this codec is based on the generation of excitations. The generated excitations are used as input to the LP synthesis filter based on past LP coefficients. In an easier way, it relies on the last frame, which is well received to generate a new frame that replaces the lost frame. The proposed PLC is designed as a PLC algorithm for G.723.1. The proposed method performance is compared with the PLC algorithm employed in G.723.1, through the Perceptual Evaluation of speech Quality (PESQ). The experiments have shown that the proposed PLC algorithm provides significant enhancement of the speech quality than the PLC of G.723.1, especially in the presence of burst packet loss and voice onset conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Packet loss

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Are Clinical T5 Models Better for Clinical Text? 4

Are Clinical T5 Models Better for Clinical Text?

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4th Machine Learning for Health Symposium, ML4H 2024

作者： Li, Yahan Harrigian, Keith Zirikly, Ayah Dredze, Mark Department of Computer Science University of Southern California United States Department of Computer Science Johns Hopkins University United States Center for Language and Speech Processing Whiting School of Engineering Johns Hopkins University Malone Center for Engineering in Healthcare Johns Hopkins University United States

Large language models with a transformer-based encoder/decoder architecture, such as T5 (Raffel et al., 2023), have become standard platforms for supervised tasks. To bring these technologies to the clinical domain, recent work has trained new (Lehman et al., 2023) or adapted existing (Lu et al., 2022) models to clinical data. However, the evaluation of these clinical T5 models and comparison to other models has been limited. Are the clinical T5 models better choices than FLAN-tuned (Chung et al., 2022a) generic T5 models? Do they generalize better to new clinical domains that differ from the training sets? We comprehensively evaluate these models across several clinical tasks and domains. We find that clinical T5 models provide marginal improvements over existing models, and perform worse when evaluated on different domains. Our results inform future choices in developing clinical LLMs. © 2024 Y. Li, K. Harrigian, A. Zirikly & M. Dredze.

关键词： Domain Generalization language Models Pre-training

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EEVEE: An Easy Annotation Tool for Natural language processing 18

EEVEE: An Easy Annotation Tool for Natural Language Processi...

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18th Linguistic Annotation Workshop, LAW 2024

作者： Sorensen, Axel Peng, Siyao Plank, Barbara van der Goot, Rob Department of Computer Science IT University of Copenhagen Denmark Munich Germany MaiNLP Center for Information and Language Processing LMU Munich Germany

ISBN: (纸本)9798891760738

Annotation tools are the starting point for creating Natural language processing (NLP) datasets. There is a wide variety of tools available;setting up these tools is however a hindrance. We propose EEVEE, an annotation tool focused on simplicity, efficiency, and ease of use. It can run directly in the browser (no setup required) and uses tab-separated files (as opposed to character offsets or task-specific formats) for annotation. It allows for annotation of multiple tasks on a single dataset and supports four task-types: sequence labeling, span labeling, text classification and seq2seq. © 2024 Association for Computational Linguistics.

关键词： Classification (of information)

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DONKII: Characterizing and Detecting Errors in Instruction-Tuning Datasets 18

DONKII: Characterizing and Detecting Errors in Instruction-T...

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18th Linguistic Annotation Workshop, LAW 2024

作者： Weber-Genzel, Leon Litschko, Robert Artemova, Ekaterina Plank, Barbara MaiNLP Center for Information and Language Processing LMU Munich Germany Munich Germany Department of Computer Science IT University of Copenhagen Denmark

ISBN: (纸本)9798891760738

Instruction tuning has become an integral part of training pipelines for Large language Models (LLMs) and has been shown to yield strong performance gains. In an orthogonal line of research, Annotation Error Detection (AED) has emerged as a tool for detecting quality problems in gold standard labels. So far, however, the application of AED methods has been limited to classification tasks. It is an open question how well AED methods generalize to language generation settings, which are becoming more widespread via LLMs. In this paper, we present a first and novel benchmark for AED on instruction tuning data: DONKII. It comprises three instruction-tuning datasets enriched with error annotations by experts and semi-automatic methods. We also provide a novel taxonomy of error types for instruction-tuning data. We find that all three datasets contain clear errors, which sometimes propagate directly into instruction-tuned LLMs. We propose four AED baselines for the generative setting and evaluate them extensively on the newly introduced dataset. Our results show that the choice of the right AED method and model size is indeed crucial and derive practical recommendations for how to use AED methods to clean instruction-tuning data. © 2024 Association for Computational Linguistics.

关键词： Error detection

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Slot and Intent Detection Resources for Bavarian and Lithuanian: Assessing Translations vs Natural Queries to Digital Assistants 30

Slot and Intent Detection Resources for Bavarian and Lithuan...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on language Resources and Evaluation, LREC-COLING 2024

作者： Winkler, Miriam Juozapaityte, Virginija van der Goot, Rob Plank, Barbara MaiNLP Lab Center for Information and Language Processing LMU Munich Germany Munich Germany Department of Computer Science IT University of Copenhagen Denmark

ISBN: (纸本)9782493814104

Digital assistants perform well in high-resource languages like English, where tasks like slot and intent detection (SID) are well-supported. Many recent SID datasets start including multiple language varieties. However, it is unclear how realistic these translated datasets are. Therefore, we extend one such dataset, namely xSID-0.4 (van der Goot et al., 2021a;Aepli et al., 2023), to include two underrepresented languages: Bavarian, a German dialect, and Lithuanian, a Baltic language. Both language variants have limited speaker populations and are often not included in multilingual projects. In addition to translations we provide "natural" queries to digital assistants generated by native speakers. We further include utterances from another dataset for Bavarian to build the richest SID dataset available today for a low-resource dialect without standard orthography. We then set out to evaluate models trained on English in a zero-shot scenario on our target language variants. Our evaluation reveals that translated data can produce overly optimistic scores. However, the error patterns in translated and natural datasets are highly similar. Cross-dataset experiments demonstrate that data collection methods influence performance, with scores lower than those achieved with single-dataset translations. This work contributes to enhancing SID datasets for underrepresented languages, yielding NaLiBaSID, a new evaluation dataset for Bavarian and Lithuanian. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Translation (languages)

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IndirectQA: Understanding Indirect Answers to Implicit Polar Questions in French and Spanish 30

IndirectQA: Understanding Indirect Answers to Implicit Polar...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on language Resources and Evaluation, LREC-COLING 2024

作者： Müller, Christin Plank, Barbara MaiNLP Lab Center for Information and Language Processing LMU Munich Germany Munich Germany Department of Computer Science IT University of Copenhagen Denmark

ISBN: (纸本)9782493814104

Polar questions are common in dialogue and expect exactly one of two answers (yes/no). It is however not uncommon for speakers to bypass these expected choices and answer, for example, "Islands are generally by the sea" to the question: "An island? By the sea?". While such answers are natural in spoken dialogues, conversational systems still struggle to interpret them. Seminal work to interpret indirect answers were made in recent years-but only for English and with strict question formulations. In this work, we present a new corpus for French and Spanish-IndirectQA -where we mine subtitle data for indirect answers to study the labeling task with six different labels, while broadening polar questions to include also implicit polar questions (statements that trigger a yes/no-answer which are not necessarily formulated as a question). We opted for subtitles since they are a readily available source of conversation in various languages, but also come with peculiarities and challenges which we will discuss. Overall, we provide the first results on French and Spanish. They show that the task is challenging: the baseline accuracy scores drop from 61.43 on English to 44.06 for French and Spanish. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Annotation Corpora Indirect Answers Natural language Understanding

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