Empirical Study of Mutual Reinforcement Effect and Application in
  Few-shot Text Classification Tasks via Prompt
The Mutual Reinforcement Effect (MRE) investigates the synergistic
relationship between word-level and text-level classifications in text
classification tasks. It posits that the performance of both classification
levels can be mutually enhanced. However, this mechanism has not been
adequately demonstrated or explained in prior research. To address this gap, we
employ empirical experiment to observe and substantiate the MRE theory. Our
experiments on 21 MRE mix datasets revealed the presence of MRE in the model
and its impact. Specifically, we conducted compare experiments use fine-tune.
The results of findings from comparison experiments corroborates the existence
of MRE. Furthermore, we extended the application of MRE to prompt learning,
utilizing word-level information as a verbalizer to bolster the model's
prediction of text-level classification labels. In our final experiment, the
F1-score significantly surpassed the baseline in 18 out of 21 MRE Mix datasets,
further validating the notion that word-level information enhances the language
model's comprehension of the text as a whole.
670fa23af41938077c9f1c5a
Chengguang Gan
claimed_verified
2024-10-17T11:26:41.308Z
629311a945f405d06678224b
https://cdn-avatars.huggingface.co/v1/production/uploads/1661878264498-629311a945f405d06678224b.png
Chengguang Gan
user
ganchengguang
670fa23af41938077c9f1c5b
Tatsunori Mori
Empirical Study of Mutual Reinforcement Effect and Application in
  Few-shot Text Classification Tasks via Prompt
The Mutual Reinforcement Effect (MRE) investigates the synergistic
relationship between word-level and text-level classifications in text
classification tasks. It posits that the performance of both classification
levels can be mutually enhanced. However, this mechanism has not been
adequately demonstrated or explained in prior research. To address this gap, we
employ empirical experiment to observe and substantiate the MRE theory. Our
experiments on 21 MRE mix datasets revealed the presence of MRE in the model
and its impact. Specifically, we conducted compare experiments use fine-tune.
The results of findings from comparison experiments corroborates the existence
of MRE. Furthermore, we extended the application of MRE to prompt learning,
utilizing word-level information as a verbalizer to bolster the model's
prediction of text-level classification labels. In our final experiment, the
F1-score significantly surpassed the baseline in 18 out of 21 MRE Mix datasets,
further validating the notion that word-level information enhances the language
model's comprehension of the text as a whole.
The study substantiates the Mutual Reinforcement Effect, demonstrating that word-level and text-level classifications can enhance each other's performance, and extends this concept to prompt learning, improving text classification F1-scores.
Mutual Reinforcement Effect word-level classifications text-level classifications prompt learning verbalizer