Papers with bidirectional long short-term memory
Opinion Mining with Deep Contextualized Embeddings (N19-3)
Copied to clipboard
| Challenge: | Existing methods for opinion expression detection are based on token-level sequence labeling . |
| Approach: | They propose to use BERT and conditional random field embedders to detect opinion expressions. |
| Outcome: | The proposed model outperforms ELMo embedders in opinion expression detection. |
TemporalVLM: Video LLMs for Temporal Reasoning in Long Videos (2026.findings-acl)
Copied to clipboard
| Challenge: | Several video understanding applications require the ability of temporal reasoning. |
| Approach: | They propose a video large language model for temporal reasoning and fine-grained understanding in long videos. |
| Outcome: | The proposed model outperforms existing methods in time and motion studies and temporal action segmentation evaluations. |
Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for stance detection are struggling to cope with the data across targets. |
| Approach: | They propose a model that uses external knowledge as a bridge to enable knowledge transfer across different targets. |
| Outcome: | The proposed model outperforms existing methods on a large real-world dataset. |
Deep Exhaustive Model for Nested Named Entity Recognition (D18-1)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is a task of finding entities with specific semantic types such as Protein, Cell, and RNA in text. |
| Approach: | They propose a deep neural model for nested named entity recognition . they enumerate all possible regions or spans as potential entity mentions . |
| Outcome: | The proposed model outperforms state-of-the-art models on nested and flat NER . it achieves 77.1% and 78.4% respectively in terms of F-score, without external knowledge resources. |
Deep Unknown Intent Detection with Margin Loss (P19-1)
Copied to clipboard
| Challenge: | Existing methods for detecting unknown intents are difficult due to lack of examples. |
| Approach: | They propose a method for detecting unknown intents using bidirectional long-term memory networks with the margin loss as the feature extractor. |
| Outcome: | The proposed method can yield consistent improvements on two benchmark datasets. |
Progress in Multilingual Speech Recognition for Low Resource Languages Kurmanji Kurdish, Cree and Inuktut (2022.lrec-1)
Copied to clipboard
| Challenge: | Using acoustic data, we develop automatic speech recognition systems for three low resource languages. |
| Approach: | They develop automatic speech recognition systems for three low resource languages using acoustic training data from 12 different languages in the hybrid DNN/HMM framework. |
| Outcome: | The proposed models are for three low resource languages: Kurmanji Kurdish, Cree and Inuktut. |