PingPong: A Natural Benchmark for Multi-Turn Code-Switching Dialogues
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911396090871808 |
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| author | Farhansyah, Mohammad Rifqi Zhafran, Hanif Muhammad Adilazuarda, Farid Muhammad, Shamsuddeen Hassan Mukhtar, Maryam Ibrahim Ousidhoum, Nedjma Winata, Genta Indra Purwarianti, Ayu Aji, Alham Fikri |
| author_facet | Farhansyah, Mohammad Rifqi Zhafran, Hanif Muhammad Adilazuarda, Farid Muhammad, Shamsuddeen Hassan Mukhtar, Maryam Ibrahim Ousidhoum, Nedjma Winata, Genta Indra Purwarianti, Ayu Aji, Alham Fikri |
| contents | Code-switching is a widespread practice among the world's multilingual majority, yet few benchmarks accurately reflect its complexity in everyday communication. We present PingPong, a benchmark for natural multi-party code-switching dialogues covering five language-combination variations, some of which are trilingual. Our dataset consists of human-authored conversations among 2 to 4 participants covering authentic, multi-threaded structures where replies frequently reference much earlier points in the dialogue. We demonstrate that our data is significantly more natural and structurally diverse than machine-generated alternatives, offering greater variation in message length, speaker dominance, and reply distance. Based on these dialogues, we define three downstream tasks: Question Answering, Dialogue Summarization, and Topic Classification. Evaluations of several state-of-the-art language models on PingPong reveal that performance remains limited on code-switched inputs, underscoring the urgent need for more robust NLP systems capable of addressing the intricacies of real-world multilingual discourse. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17277 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PingPong: A Natural Benchmark for Multi-Turn Code-Switching Dialogues Farhansyah, Mohammad Rifqi Zhafran, Hanif Muhammad Adilazuarda, Farid Muhammad, Shamsuddeen Hassan Mukhtar, Maryam Ibrahim Ousidhoum, Nedjma Winata, Genta Indra Purwarianti, Ayu Aji, Alham Fikri Computation and Language Code-switching is a widespread practice among the world's multilingual majority, yet few benchmarks accurately reflect its complexity in everyday communication. We present PingPong, a benchmark for natural multi-party code-switching dialogues covering five language-combination variations, some of which are trilingual. Our dataset consists of human-authored conversations among 2 to 4 participants covering authentic, multi-threaded structures where replies frequently reference much earlier points in the dialogue. We demonstrate that our data is significantly more natural and structurally diverse than machine-generated alternatives, offering greater variation in message length, speaker dominance, and reply distance. Based on these dialogues, we define three downstream tasks: Question Answering, Dialogue Summarization, and Topic Classification. Evaluations of several state-of-the-art language models on PingPong reveal that performance remains limited on code-switched inputs, underscoring the urgent need for more robust NLP systems capable of addressing the intricacies of real-world multilingual discourse. |
| title | PingPong: A Natural Benchmark for Multi-Turn Code-Switching Dialogues |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.17277 |