| Challenge: | In computational linguistics, nounnoun compound interpretation is approached as an automatic classification problem. |
| Approach: | They empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task. |
| Outcome: | The proposed methods improve the accuracy of a neural classifier and its F1 scores on the less frequent, but more difficult relations. |
Similar Papers
Can Large Language Models Interpret Noun-Noun Compounds? A Linguistically-Motivated Study on Lexicalized and Novel Compounds (2024.acl-long)
Copied to clipboard
| Challenge: | Noun-noun compounds represent an important challenge for Natural Language Understanding . correct interpretation of noun-nomin compounds is essential for many applications . |
| Approach: | They test whether Large Language Models can interpret the semantic relation between nouns . they also test whether they can abstract from such knowledge to predict the relation . |
| Outcome: | The proposed models can interpret the semantic relation between nouns and compounds using analogical comparisons. |
What can we learn from Semantic Tagging? (D18-1)
Copied to clipboard
| Challenge: | a recent study shows that multi-task learning improves performance of NLP tasks by exploiting similarities between tasks. |
| Approach: | They employ semantic tagging as an auxiliary task for three NLP tasks . they compare full neural network sharing, partial neural network shared and learning what to share . |
| Outcome: | The proposed model improves for part-of-speech tagging, universal dependency parsing and natural language inference. |
Olive Oil is Made of Olives, Baby Oil is Made for Babies: Interpreting Noun Compounds Using Paraphrases in a Neural Model (N18-2)
Copied to clipboard
| Challenge: | Recent work suggests that success stems from memorizing single prototypical words for each relation. |
| Approach: | They propose a neural paraphrasing approach that maps NCs to paraphrases that express the relation between constituent words. |
| Outcome: | The proposed method performs better when memorization is not possible. |
Towards a Standardized Dataset for Noun Compound Interpretation (L18-1)
Copied to clipboard
| Challenge: | Noun compounds are interesting constructs in Natural Language Processing . lack of standardized set of relation inventories and annotated datasets hinders interpretation . |
| Approach: | They propose a dataset that uses FrameNet as its semantic relation inventory to examine noun compounds. |
| Outcome: | The proposed dataset is linguistically grounded and uses FrameNet as its semantic relation inventory. |
Do Text-to-Text Multi-Task Learners Suffer from Task Conflict? (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing multi-task learning architectures learn a single model across multiple tasks through a shared encoder followed by task-specific decoders. |
| Approach: | They propose to use a shared encoder and language model decoder to learn a single model across multiple tasks. |
| Outcome: | The proposed architecture does surprisingly well across a range of diverse tasks. |
Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations (P18-1)
Copied to clipboard
| Challenge: | Existing methods for paraphrasing nouncompounds lack the ability to generalize and have a hard time interpreting infrequent or new noun-compound. |
| Approach: | They propose a neural model that generalizes better by representing paraphrases in a continuous space, generalizing for both unseen noun-compounds and rare paraphrase. |
| Outcome: | The proposed model generalizes better by representing paraphrases in a continuous space, generalizing for unseen noun-compounds and rare paraphrase. |
Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces (N18-1)
Copied to clipboard
| Challenge: | Multi-task learning and semi-supervised learning are successful paradigms for learning in scenarios with limited labelled data. |
| Approach: | They propose to induce a joint embedding space between disparate label spaces and learning transfer functions between label embeddments to leverage unlabelled data and auxiliary, annotated datasets. |
| Outcome: | The proposed approach outperforms strong single and multi-task baselines and achieves state of the art on aspect-based and topic-based sentiment analysis. |
Multitask Learning for Cross-Lingual Transfer of Broad-coverage Semantic Dependencies (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for developing broad-coverage semantic dependency parsers for languages without semantically annotated data are limited to English, Czech and Chinese. |
| Approach: | They propose a multitask learning framework coupled with annotation projection to build broad-coverage semantic dependency parsers for languages without annotated resources. |
| Outcome: | The proposed model improves labeled F1 score on multitask tasks from English to Czech compared to baseline models . |
From chocolate bunny to chocolate crocodile: Do Language Models Understand Noun Compounds? (2023.findings-acl)
Copied to clipboard
| Challenge: | Noun compound interpretation is the task of expressing a noun compound in a free-text paraphrase that makes the relationship between the constituent nouns explicit. |
| Approach: | They propose modifications to the standard task and propose a new task that solves it. |
| Outcome: | The proposed task solves the standard task of paraphrasing a noun compound in a free-text paraphrase that makes the relationship between the constituent nouns explicit. |
Exploring and Predicting Transferability across NLP Tasks (2020.emnlp-main)
Copied to clipboard
Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, Mohit Iyyer
| Challenge: | Recent advances in NLP demonstrate the effectiveness of training large-scale language models and transferring them to downstream tasks. |
| Approach: | They conduct an extensive study of the transferability between 33 NLP tasks across three broad classes of problems. |
| Outcome: | The proposed model can improve performance even with low-data source tasks that differ substantially from the target task. |