title: BiPhone: Modeling Inter Language Phonetic Influences in Text
summary: 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.
paper_authors._id: 64ab8f63d4a402e8dcf714f5
paper_authors.name: Abhirut Gupta
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-11T07:54:45.400Z
paper_authors._id: 6478422279f2d49511eac716
paper_authors.avatarUrl: /avatars/626ac5f5881347e0c233ecfb3c74c4c7.svg
paper_authors.fullname: Gupta
paper_authors.type: user
paper_authors.user: Abhirut
paper_authors._id: 64ab8f63d4a402e8dcf714f6
paper_authors.name: Ananya B. Sai
paper_authors._id: 64ab8f63d4a402e8dcf714f7
paper_authors.name: Richard Sproat
paper_authors._id: 64ab8f63d4a402e8dcf714f8
paper_authors.name: Yuri Vasilevski
paper_authors._id: 64ab8f63d4a402e8dcf714f9
paper_authors.name: James S. Ren
paper_authors._id: 64ab8f63d4a402e8dcf714fa
paper_authors.name: Ambarish Jash
paper_authors._id: 64ab8f63d4a402e8dcf714fb
paper_authors.name: Sukhdeep S. Sodhi
paper_authors._id: 64ab8f63d4a402e8dcf714fc
paper_authors.name: Aravindan Raghuveer
paper_title: BiPhone: Modeling Inter Language Phonetic Influences in Text
paper_summary: 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.
paper_ai_keywords: phoneme confusions generative model Bi-Phone corrupted L2 text human evaluations SuperGLUE FunGLUE phonetically robust language models phoneme prediction pre-training task byte models FunGLUE benchmark