21 accepted papers
Large language models (LLMs), through pretraining on extensive corpora, encompass rich semantic knowledge and exhibit the potential for efficient adaptation to diverse downstream tasks. However, the intrinsic mechanisms underlying LLMs remain unexplored, limiting the efficacy of applying these model…
The majority of the current mispronunciation detection and diagnosis (MD&D) methods rely on manually annotated data for model training. However, annotating mispronunciations produced by second language (L2) learners is costly. Consequently, data scarcity emerges as a significant challenge in MD&D ta…
In the constant updates of the product dialogue systems, we need to retrain the natural language understanding (NLU) model as new data from the real users would be merged into the existing data accumulated in the last updates. Within the newly added data, new intents would emerge and might have sema…
In this paper, we uncover a positional bias in the evaluation paradigm of adopting large language models (LLMs), e.g., GPT-4, as a referee to score and compare the quality of responses generated by candidate models. We find that the quality ranking of candidate responses can be easily hacked by simp…
Large Language Models (LLMs) have emerged as powerful tools for Text-to-SQL tasks, exhibiting remarkable reasoning capabilities. Different from tasks such as math word problem and commonsense reasoning, SQL solutions have a relatively fixed pattern. This facilitates the investigation of whether LLMs…
Pretrained language models have achieved remarkable success in natural language understanding. However, fine-tuning pretrained models on limited training data tends to overfit and thus diminish performance. This paper presents Bi-Drop, a fine-tuning strategy that selectively updates model parameters…
Video multimodal fusion aims to integrate multimodal signals in videos, such as visual, audio and text, to make a complementary prediction with multiple modalities contents. However, unlike other image-text multimodal tasks, video has longer multimodal sequences with more redundancy and noise in bot…
Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or continuous integration scenarios. Prior work attempts to obtain 1-to-1 QA pairs from growing customer service chatlog, w…
Continual relation extraction (CRE) models aim at handling emerging new relations while avoiding catastrophically forgetting old ones in the streaming data. Though improvements have been shown by previous CRE studies, most of them only adopt a vanilla strategy when models first learn representations…
Automatic pronunciation assessment is commonly trained and applied for a specific language, which is not practical in multi-lingual or low-resource scenarios. In this paper, we propose a unified method to take advantage of multi-lingual data for multi-lingual pronunciation assessment. To this end, w…
To tackle the recognition error problem for Automatic speech recognition (ASR), one common approach is to utilize text-based ASR error correction methods focusing on text error pat-terns. To include the audio information for better error correction, we propose a sequence-to-sequence multi-modal ASR…
Multi-modal speech emotion recognition (SER) brings performance improvement compared with single-modal systems. However, the ASR errors from text modality may deteriorate the SER performance. This paper proposes an SER method robust to ASR errors. We explore complementary semantic in-formation from…
Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typologyfrom the source language or when pre-training data is limited in size. In this paper, we propose XLM…
While interacting with chatbots, users may elicit multiple intents in a single dialogue utterance. Instead of training a dedicated multi-intent detection model, we propose DialogUSR, a dialogue utterance splitting and reformulation task that first splits multi-intent user query into several single-i…
We present DualNER, a simple and effective framework to make full use of both annotated source language corpus and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER). In particular, we combine two complementary learning paradigms of NER, i.e., sequence labeling…
Automatic spoken language assessment plays an important role in assessing English proficiency of non-native learners, which involves tasks ranging from restricted tasks such as Repeat Sentences to more open-ended tasks such as unconstrained spontaneous speech. Traditional methods typically focus on…
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification due to its complex label hierarchy.Recently, the pretrained language models (PLM)have been widely adopted in HTC through a fine-tuning paradigm. However, in this paradigm, there exists a huge gap between th…
Phoneme segmentation plays an important role in various speech processing applications such as keyword spotting, automatic pronunciation assessment, and automatic speech recognition. In this paper, we propose a method for phoneme segmentation based on a regularized attention mechanism. Specifically,…
Continual relation extraction (CRE) aims to continually learn new relations from a class-incremental data stream. CRE model usually suffers from catastrophic forgetting problem, i.e., the performance of old relations seriously degrades when the model learns new relations. Most previous work attribut…
Phoneme mispronunciation detection plays an important role in Computer-Assisted Pronunciation Training. Traditional methods either rely on phone recognition, which has the limitation of incapability of detecting mispronounced phonemes out of the dictionary, or the need of external phoneme alignment…
Automatic pronunciation assessment plays an important role in Computer-Assisted Pronunciation Training (CAPT). Traditional methods for pronunciation assessment of reading aloud tasks utilize features derived from automatic speech recognition (ASR) and thus are sensitive to the accuracy of ASR and th…