Zobrazeno 1 - 10
of 15 473
pro vyhledávání: '"Doh AS"'
Intent classification is a text understanding task that identifies user needs from input text queries. While intent classification has been extensively studied in various domains, it has not received much attention in the music domain. In this paper,
Externí odkaz:
http://arxiv.org/abs/2411.12254
A conversational music retrieval system can help users discover music that matches their preferences through dialogue. To achieve this, a conversational music retrieval system should seamlessly engage in multi-turn conversation by 1) understanding us
Externí odkaz:
http://arxiv.org/abs/2411.07439
Autor:
Bang, Hayeon, Choi, Eunjin, Finch, Megan, Doh, Seungheon, Lee, Seolhee, Lee, Gyeong-Hoon, Nam, Juhan
While piano music has become a significant area of study in Music Information Retrieval (MIR), there is a notable lack of datasets for piano solo music with text labels. To address this gap, we present PIAST (PIano dataset with Audio, Symbolic, and T
Externí odkaz:
http://arxiv.org/abs/2411.02551
Text-to-Music Retrieval, finding music based on a given natural language query, plays a pivotal role in content discovery within extensive music databases. To address this challenge, prior research has predominantly focused on a joint embedding of mu
Externí odkaz:
http://arxiv.org/abs/2410.03264
Recent psycholinguistic research has compared human reading times to surprisal estimates from language models to study the factors shaping human sentence processing difficulty. Previous studies have shown a strong fit between surprisal values from Tr
Externí odkaz:
http://arxiv.org/abs/2409.11250
We tackle the challenges of decentralized multi-robot navigation in environments with nonconvex obstacles, where complete environmental knowledge is unavailable. While reactive methods like Artificial Potential Field (APF) offer simplicity and effici
Externí odkaz:
http://arxiv.org/abs/2409.10332
Autor:
Albert, Julien, Balfroid, Martin, Doh, Miriam, Bogaert, Jeremie, La Fisca, Luca, De Vos, Liesbet, Renard, Bryan, Stragier, Vincent, Jean, Emmanuel
Recommender systems have become integral to our digital experiences, from online shopping to streaming platforms. Still, the rationale behind their suggestions often remains opaque to users. While some systems employ a graph-based approach, offering
Externí odkaz:
http://arxiv.org/abs/2409.06297
For large language models (LLMs) like NLLB and GPT, translating idioms remains a challenge. Our goal is to enhance translation fidelity by improving LLM processing of idiomatic language while preserving the original linguistic style. This has a signi
Externí odkaz:
http://arxiv.org/abs/2407.03518
Autor:
Doh, Miriam, Karagianni, Anastasia
This study delves into gender classification systems, shedding light on the interaction between social stereotypes and algorithmic determinations. Drawing on the "averageness theory," which suggests a relationship between a face's attractiveness and
Externí odkaz:
http://arxiv.org/abs/2407.17474
Autor:
Oh, Byung-Doh, Schuler, William
Predictions of word-by-word conditional probabilities from Transformer-based language models are often evaluated to model the incremental processing difficulty of human readers. In this paper, we argue that there is a confound posed by the most commo
Externí odkaz:
http://arxiv.org/abs/2406.10851