Guiding Zero-Shot Paraphrase Generation with Fine-Grained Control Tokens (2023.starsem-1)
Copied to clipboard
| Challenge: | Sequence-to-sequence paraphrase generation models struggle with the generation of diverse paraphrases. |
| Approach: | They propose a translation-based guided paraphrase generation model that learns useful features for promoting surface form variation in generated paraphrases from cross-lingual parallel data. |
| Outcome: | The proposed model learns useful features for promoting surface form variation in generated paraphrases from cross-lingual parallel data. |
Similar Papers
Paraphrase Identification via Textual Inference (2024.starsem-1)
Copied to clipboard
| Challenge: | Paraphrase identification (PI) and natural language inference (NLI) are important tasks in natural language processing. |
| Approach: | They propose a method for paraphrase identification and natural language inference using an NLI system to solve these tasks. |
| Outcome: | The proposed method outperforms dedicated PI models on PI datasets and provides insights into limitations of current benchmarks. |
Multilingual Neural Semantic Parsing for Low-Resourced Languages (2021.starsem-1)
Copied to clipboard
| Challenge: | a large amount of training data is needed to understand multilingual semantic parsing models. |
| Approach: | They propose to use machine translation to bootstrap multilingual training data from English data. |
| Outcome: | The proposed model outperforms existing models on human-written sentences and the state-of-the-art models on the public NLMaps dataset. |
Testing Paraphrase Models on Recognising Sentence Pairs at Different Degrees of Semantic Overlap (2023.starsem-1)
Copied to clipboard
| Challenge: | Existing models for paraphrase detection are not suitable for many applications . existing datasets ignore and fail to test models in this setup . |
| Approach: | They propose to use adversarial paradigms to test paraphrase detection models . they propose to examine the sensitivity to different degrees of semantic overlap . |
| Outcome: | Empirical results show that paraphrase models and different sentence encoders appear successful on evaluations, but measuring the degree of semantic overlap remains a big challenge for them. |
One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets (2021.starsem-1)
Copied to clipboard
| Challenge: | Existing semantic parsing datasets lack a single standard for meaning representations . lack of a standard led to the creation of plethora of datasets requiring expert annotators . |
| Approach: | They propose to use multi-task learning to unify different datasets and train a single model for them. |
| Outcome: | The proposed architectures yield better parsing accuracies and composition generalization than single-task models. |
Inducing Language-Agnostic Multilingual Representations (2021.starsem-1)
Copied to clipboard
| Challenge: | Cross-lingual representations have the potential to make NLP techniques available to the vast majority of languages in the world, but they currently require large pretraining corpora or access to typologically similar languages. |
| Approach: | They propose to remove language identity signals from multilingual embeddings by re-aligning vector spaces of target languages to a pivot source language and removing language-specific means and variances. |
| Outcome: | The proposed approaches reduce cross-lingual transfer gap by 8.9 points (m-BERT) and 18.2 points (XLM-R) on average across all tasks and languages. |
Comparison and Combination of Sentence Embeddings Derived from Different Supervision Signals (2022.starsem-1)
Copied to clipboard
| Challenge: | Existing methods to derive sentence embeddings have not been well understood what properties are captured in the resulting sentences depending on the supervision signals. |
| Approach: | They propose to combine two types of sentence embedding methods with similar architectures and tasks to investigate their properties. |
| Outcome: | The proposed methods perform better on unsupervised and downstream tasks than the proposed methods on untrained STS tasks and probing tasks. |
Measuring Alignment Bias in Neural Seq2seq Semantic Parsers (2022.starsem-1)
Copied to clipboard
| Challenge: | Sequence-to-sequence semantic parsers with attention mechanisms have changed the research landscape . emergence of seq2seq models have led to questions about alignments . |
| Approach: | They investigate whether seq2seq models can handle both simple and complex alignments. |
| Outcome: | The proposed model performs better on monotonic and complex alignments compared to monotonic models . |
Fine-tuning BERT with Focus Words for Explanation Regeneration (2020.starsem-1)
Copied to clipboard
| Challenge: | Existing approaches to explain the correct answer in multiple-choice QA are low in F-scores and lack of performance. |
| Approach: | They introduce a lightweight focus feature in a transformer-based NLP model and examine performance improvements. |
| Outcome: | The proposed model achieves the highest scores, second only to a computationally intensive system. |
VOLIMET: A Parallel Corpus of Literal and Metaphorical Verb-Object Pairs for English–German and English–French (2024.starsem-1)
Copied to clipboard
| Challenge: | Metaphorical language is a complex interplay of cultural and linguistic elements that characterizes metaphorical language . a corpus of parallel sentences containing gold standard alignments of metaphorical verb-object pairs and literal paraphrases is presented . |
| Approach: | They propose to analyze metaphorical verb-object pairs and literal paraphrases in parallel sentences from English to German and French. |
| Outcome: | The proposed corpus of 2,916 parallel sentences reveals monolingual patterns for metaphorical vs. literal uses in English . cross-lingually, the results show a rich variability in translations as well as different behaviors for the two target languages . |
Semantics-aware Attention Improves Neural Machine Translation (2022.starsem-1)
Copied to clipboard
| Challenge: | Existing attempts to integrate semantic structures into NMT Transformers have failed . |
| Approach: | They propose two parameter-free methods for injecting semantic information into Transformers, using a Scene-Aware Self-Attention (SASA) head and a Scenario-Award Cross-Action (SACrA) head. |
| Outcome: | The proposed methods improve on the vanilla Transformer and syntax-aware models for four language pairs and show an additional gain when using both semantic and syntactic structures in some language pairs. |