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Dialogue Understanding

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/akreal/bloomzmms

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Teaching a Multilingual Large Language Model to Understand Multilingual Speech via Multi-Instructional Training

https://github.com/akshittyagi/intclass

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Fast Intent Classification for Spoken Language Understanding

https://github.com/ala-na/media_benchmark_intent_annotations

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New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark

https://github.com/alanbaade/SyllableLM

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SyllableLM: Learning Coarse Semantic Units for Speech Language Models

https://github.com/alexa/alexa-dataset-contextual-query-rewrite

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A dataset for resolving referring expressions in spoken dialogue via contextual query rewrites (CQR)

https://github.com/alexa/teach

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TEACh: Task-driven Embodied Agents that Chat

https://github.com/alibaba-nlp/aishell-ner

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AISHELL-NER: Named Entity Recognition from Chinese Speech

https://github.com/alon-albalak/tlidb

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FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue

https://github.com/alumae/torch-xvectors-wav

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VOXLINGUA107: A DATASET FOR SPOKEN LANGUAGE RECOGNITION

https://github.com/amazon-research/dse

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Learning Dialogue Representations from Consecutive Utterances

https://github.com/amazon-science/zero-shot-e2e-slu

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Zero-Shot End-to-End Spoken Language Understanding via Cross-Modal Selective Self-Training

https://github.com/amphionspace/sd-eval

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SD-Eval: A Benchmark Dataset for Spoken Dialogue Understanding Beyond Words

https://github.com/anhtunguyen98/bislu

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Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation

https://github.com/ankitapasad/layerwise-analysis

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Comparative layer-wise analysis of self-supervised speech models

https://github.com/anupsingh15/BEST-STD

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BEST-STD: Bidirectional Mamba-Enhanced Speech Tokenization for Spoken Term Detection

https://github.com/apple/dmel

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dMel: Speech Tokenization made Simple

https://github.com/apple/ml-cread

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CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues

https://github.com/asappresearch/slue-toolkit

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SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech

https://github.com/asappresearch/spoken-ner

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On the Use of External Data for Spoken Named Entity Recognition

https://github.com/ashi-ta/speechglue

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SpeechGLUE: How Well Can Self-Supervised Speech Models Capture Linguistic Knowledge?

https://github.com/babelscape/alert

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ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming

https://github.com/bashlab/quads

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QUADS: QUAntized Distillation Framework for Efficient Speech Language Understanding

https://github.com/bhigy/textual-supervision

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Textual Supervision for Visually Grounded Spoken Language Understanding

https://github.com/bothe/EDAs

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EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators

https://github.com/bothe/dialogue-act-recognition

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Discourse-Wizard: Discovering Deep Discourse Structure in your Conversation with RNNs

https://github.com/briansidp/espnet

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End-to-end Spoken Language Understanding with Tree-constrained Pointer Generator

https://github.com/chiahsuan156/Spoken-SQuAD

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Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehension

https://github.com/clovaai/textual-kd-slu

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Two-stage Textual Knowledge Distillation for End-to-End Spoken Language Understanding

https://github.com/cpjku/efficientleaf

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EfficientLEAF: A Faster LEarnable Audio Frontend of Questionable Use

https://github.com/cwang621/blsp

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BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation Writing

https://github.com/declare-lab/M2H2-dataset

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M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations

https://github.com/declare-lab/dialogue-understanding

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Utterance-level Dialogue Understanding: An Empirical Study

https://github.com/declare-lab/segue

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Sentence Embedder Guided Utterance Encoder (SEGUE) for Spoken Language Understanding

https://github.com/digitalphonetics/multilingual-seq2seq-slu

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Leveraging Multilingual Self-Supervised Pretrained Models for Sequence-to-Sequence End-to-End Spoken Language Understanding

https://github.com/dingdongwang/mmsu_bench

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MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark

https://github.com/dongguanting/noise-slot-filling-llm

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Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Task

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