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Extracting Event Temporal Relations via Hyperbolic Geometry ...
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42 |
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging ...
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44 |
Open Aspect Target Sentiment Classification with Natural Language Prompts ...
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Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you? ...
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We've had this conversation before: A Novel Approach to Measuring Dialog Similarity ...
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ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations ...
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CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization ...
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Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss ...
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53 |
Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention ...
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54 |
Analyzing the Surprising Variability in Word Embedding Stability Across Languages ...
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55 |
Neural Machine Translation with Heterogeneous Topic Knowledge Embeddings ...
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56 |
Towards Zero-Shot Knowledge Distillation for Natural Language Processing ...
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SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.401/ Abstract: Next generation task-oriented dialog systems need to understand conversational contexts with their perceived surroundings, to effectively help users in the real-world multimodal environment. Existing task-oriented dialog datasets aimed towards virtual assistance fall short and do not situate the dialog in the user's multimodal context. To overcome, we present a new dataset for Situated and Interactive Multimodal Conversations, SIMMC 2.0, which includes 11K task-oriented user<>assistant dialogs (117K utterances) in the shopping domain, grounded in immersive and photo-realistic scenes. The dialogs are collected using a two-phase pipeline: (1) A novel multimodal dialog simulator generates simulated dialog flows, with an emphasis on diversity and richness of interactions, (2) Manual paraphrasing of the generated utterances to collect diverse referring expressions. We provide an in-depth analysis of the collected dataset, and ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://dx.doi.org/10.48448/ys4f-xb35 https://underline.io/lecture/37798-simmc-2.0-a-task-oriented-dialog-dataset-for-immersive-multimodal-conversations
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59 |
Automatic Text Evaluation through the Lens of Wasserstein Barycenters ...
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Combining sentence and table evidence to predict veracity of factual claims using TaPaS and RoBERTa ...
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