Name

Naoki Yoshinaga

Laboratory

Institute of Industrial Science (IIS)

Research

Natural Language Processing

Fast and Accurate Natura Language Processing
Languages allow us to think and record our experiences in society to share them with others. To decipher
humans and the world from their countless writings and assist our language activities, our laboratory studies
natural language processing (NLP) to “compute” language eﬃciently and accurately. The pursuit of fast,compact, yet accurate models reveals the “shape” of languages that behave regularly and stochastically(computational linguistics (CL)), ultimately leading to understanding and improving human intelligence.
https://www.tkl.iis.u-tokyo.ac.jp/˜ynaga/index.en.html

We think studies by students with diﬀerent linguistic, cultural, academic backgrounds, from their own perspectives elucidate languages; students are able (and expected) to voluntarily set their research themes.
We welcome those who tackle novel NLP tasks, not tied to accuracy-driven studies on worn-out benchmarks.

Read overﬂowing textual information

Speak languages as in the wild

Humans generate tons of real-world information
via microblogs (social big data), in which quality
and quantity change over time. We leverage data
structures and algorithms in computer science torealize the fastest NLP in the world. Speciﬁcally,
we have invented a classiﬁer that becomes faster as
the redundancy in text increases, and realized a fundamental text processing that processes 1,000,000
sentences per second. Meanwhile, we have established a method that instantly ﬁnds the emergenceand disappearance of real-world entities from text.
The infrastructure for analyzing social big data
built upon our technology helps us understand situations in disasters such as the 3.11 earthquake andpublic opinions during the COVID-19 pandemic.

The meaning of text depends on the contexts,
namely, whom, when, where, and to whom the text
is written. Although recent deep learning pursues alarge-scale training to generalize models, thus making large language models (LLMs) as the one-sizeﬁts-all model, it is diﬃcult to adapt those LLMs toindividual language contexts, as kana-kanji conversion and predictive typing are personalized. We thus
deconstruct LLMs to realize adaptive technology
to handle languages in the wild.
Speciﬁcally, we analyze, compress, and deconstruct models and study retrieval-augmented generation for the model to acquire knowledge as needed
and context-aware text generation and its evaluation
based on user, space, time, and multimodal contexts.
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Our laboratory has been collaborating with Toyoda and Goda laboratories in IIS, and sharing research
environments (data, computational resources, and student rooms). The members will receive feedback fortheir research from various perspectives, and can perform out-of-the-box, cross-cutting research.
For inquiry, feel free to contact Naoki Yoshinaga (ynaga@iis.u-tokyo.ac.jp).