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Found 15 papers, 13 papers with code
Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification
Dialogue intent classification aims to identify the underlying purpose or intent of a user's input in a conversation.
Efficient Dynamic Hard Negative Sampling for Dialogue Selection
Recent studies have demonstrated significant improvements in selection tasks, and a considerable portion of this success is attributed to incorporating informative negative samples during training.
Ranked #1 on
Conversational Response Selection
on E-commerce
Deep Exploration of Cross-Lingual Zero-Shot Generalization in Instruction Tuning
Subsequently, we assess the performance on unseen tasks in a language different from the one used for training.
MVMR: A New Framework for Evaluating Faithfulness of Video Moment Retrieval against Multiple Distractors
However, the existing VMR framework evaluates video moment retrieval performance, assuming that a video is given, which may not reveal whether the models exhibit overconfidence in the falsely given video.
Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners
Meta-training, which fine-tunes the language model (LM) on various downstream tasks by maximizing the likelihood of the target label given the task instruction and input instance, has improved the zero-shot task generalization performance.
Ranked #2 on
Question Answering
on StoryCloze
External Knowledge Selection with Weighted Negative Sampling in Knowledge-grounded Task-oriented Dialogue Systems
In the experiment, we investigate the effect of weighted negative sampling, post-training, and style transfer.
TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models
Language Models (LMs) become outdated as the world changes; they often fail to perform tasks requiring recent factual information which was absent or different during training, a phenomenon called temporal misalignment.
Towards Continual Knowledge Learning of Language Models
By highlighting the critical causes of knowledge forgetting, we show that CKL is a challenging and important problem that helps us better understand and train ever-changing LMs.
Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech
Although early text-to-speech (TTS) models such as Tacotron 2 have succeeded in generating human-like speech, their autoregressive (AR) architectures have a limitation that they require a lot of time to generate a mel-spectrogram consisting of hundreds of steps.
KPQA: A Metric for Generative Question Answering Using Keyphrase Weights
To evaluate our metric, we create high-quality human judgments of correctness on two GenQA datasets.
Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning
Even though BERT achieves successful performance improvements in various supervised learning tasks, applying BERT for unsupervised tasks still holds a limitation that it requires repetitive inference for computing contextual language representations.
Effective Sentence Scoring Method using Bidirectional Language Model for Speech Recognition
Recent studies have tried to use bidirectional LMs (biLMs) instead of conventional unidirectional LMs (uniLMs) for rescoring the $N$-best list decoded from the acoustic model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder
Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume.
Improving Neural Question Generation using Answer Separation
Previous NQG models suffer from a problem that a significant proportion of the generated questions include words in the question target, resulting in the generation of unintended questions.
Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering
In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module.
Ranked #1 on
Answer Selection
on Ubuntu Dialogue (v1, Ranking)
