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Found 14 papers, 3 papers with code
Understanding In-Context Learning with a Pelican Soup Framework
In this framework, we introduce (1) the notion of a common sense knowledge base, (2) a general formalism for natural language classification tasks, and the notion of (3) meaning association.
On Retrieval Augmentation and the Limitations of Language Model Training
Augmenting a language model (LM) with $k$-nearest neighbors ($k$NN) retrieval on its training data alone can decrease its perplexity, though the underlying reasons for this remain elusive.
The Distributional Hypothesis Does Not Fully Explain the Benefits of Masked Language Model Pretraining
Via a synthetic dataset, our analysis suggests that distributional property indeed leads to the better sample efficiency of pretrained masked language models, but does not fully explain the generalization capability.
DialCrowd 2.0: A Quality-Focused Dialog System Crowdsourcing Toolkit
Dialog system developers need high-quality data to train, fine-tune and assess their systems.
Breaking Down Multilingual Machine Translation
While multilingual training is now an essential ingredient in machine translation (MT) systems, recent work has demonstrated that it has different effects in different multilingual settings, such as many-to-one, one-to-many, and many-to-many learning.
Are you doing what I say? On modalities alignment in ALFRED
ALFRED is a recently proposed benchmark that requires a model to complete tasks in simulated house environments specified by instructions in natural language.
On a Benefit of Mask Language Modeling: Robustness to Simplicity Bias
Despite the success of pretrained masked language models (MLM), why MLM pretraining is useful is still a qeustion not fully answered.
Improving Dialogue State Tracking by Joint Slot Modeling
Dialogue state tracking models play an important role in a task-oriented dialogue system.
Relating Neural Text Degeneration to Exposure Bias
This work focuses on relating two mysteries in neural-based text generation: exposure bias, and text degeneration.
Why Can You Lay Off Heads? Investigating How BERT Heads Transfer
Hence, the acceptable deduction of performance on the pre-trained task when distilling a model can be derived from the results, and we further compare the behavior of the pruned model before and after fine-tuning.
An Empirical Study of Content Understanding in Conversational Question Answering
However, to best of our knowledge, two important questions for conversational comprehension research have not been well studied: 1) How well can the benchmark dataset reflect models' content understanding?
RAP-Net: Recurrent Attention Pooling Networks for Dialogue Response Selection
The response selection has been an emerging research topic due to the growing interest in dialogue modeling, where the goal of the task is to select an appropriate response for continuing dialogues.
Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer
With the increasing research interest in dialogue response generation, there is an emerging branch formulating this task as selecting next sentences, where given the partial dialogue contexts, the goal is to determine the most probable next sentence.
Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems
Solving math word problems is a challenging task that requires accurate natural language understanding to bridge natural language texts and math expressions.
