BiPhone: Modeling Inter Language Phonetic Influences in Text
A large number of people are forced to use the Web in a language they have
low literacy in due to technology asymmetries. Written text in the second
language (L2) from such users often contains a large number of errors that are
influenced by their native language (L1). We propose a method to mine phoneme
confusions (sounds in L2 that an L1 speaker is likely to conflate) for pairs of
L1 and L2. These confusions are then plugged into a generative model (Bi-Phone)
for synthetically producing corrupted L2 text. Through human evaluations, we
show that Bi-Phone generates plausible corruptions that differ across L1s and
also have widespread coverage on the Web. We also corrupt the popular language
understanding benchmark SuperGLUE with our technique (FunGLUE for Phonetically
Noised GLUE) and show that SoTA language understating models perform poorly. We
also introduce a new phoneme prediction pre-training task which helps byte
models to recover performance close to SuperGLUE. Finally, we also release the
FunGLUE benchmark to promote further research in phonetically robust language
models. To the best of our knowledge, FunGLUE is the first benchmark to
introduce L1-L2 interactions in text.
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Abhirut Gupta
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2023-07-11T07:54:45.400Z
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Gupta
user
Abhirut
64ab8f63d4a402e8dcf714f6
Ananya B. Sai
64ab8f63d4a402e8dcf714f7
Richard Sproat
64ab8f63d4a402e8dcf714f8
Yuri Vasilevski
64ab8f63d4a402e8dcf714f9
James S. Ren
64ab8f63d4a402e8dcf714fa
Ambarish Jash
64ab8f63d4a402e8dcf714fb
Sukhdeep S. Sodhi
64ab8f63d4a402e8dcf714fc
Aravindan Raghuveer
BiPhone: Modeling Inter Language Phonetic Influences in Text
A large number of people are forced to use the Web in a language they have
low literacy in due to technology asymmetries. Written text in the second
language (L2) from such users often contains a large number of errors that are
influenced by their native language (L1). We propose a method to mine phoneme
confusions (sounds in L2 that an L1 speaker is likely to conflate) for pairs of
L1 and L2. These confusions are then plugged into a generative model (Bi-Phone)
for synthetically producing corrupted L2 text. Through human evaluations, we
show that Bi-Phone generates plausible corruptions that differ across L1s and
also have widespread coverage on the Web. We also corrupt the popular language
understanding benchmark SuperGLUE with our technique (FunGLUE for Phonetically
Noised GLUE) and show that SoTA language understating models perform poorly. We
also introduce a new phoneme prediction pre-training task which helps byte
models to recover performance close to SuperGLUE. Finally, we also release the
FunGLUE benchmark to promote further research in phonetically robust language
models. To the best of our knowledge, FunGLUE is the first benchmark to
introduce L1-L2 interactions in text.
A method for generating phonetically corrupted language text from non-native speakers improves benchmarks and highlights the need for more robust language models.
phoneme confusions generative model Bi-Phone SuperGLUE phonetically noisy FunGLUE phoneme prediction byte models