| Challenge: | Phrase alignment is the basis for sentence pair interactions, such as paraphrase identification and textual entailment recognition. |
| Approach: | They propose a phrase alignment model that embeds similarity distributions into powerful contextualized representations that can be used to model sentence pair interactions. |
| Outcome: | The proposed method significantly outperforms that used in a previous study and achieves a performance competitive with that of experienced human annotators. |
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| Challenge: | EmbedAlign model embeds words in their complete observed context and learns by marginalisation of latent lexical alignments. |
| Approach: | They exploit translation as a distributional context and embed words as posterior probability densities, rather than point estimates, which allows them to compare words in context using a measure of overlap between distributions. |
| Outcome: | The proposed model performs on a range of lexical semantics tasks and achieves competitive results on benchmarks including natural language inference, paraphrasing, and text similarity. |
Neural Network Alignment for Sentential Paraphrases (P19-1)
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| Challenge: | a monolingual alignment system is ill-suited for word- or short phrase-based alignments. |
| Approach: | They propose a monolingual alignment system for long, sentence- or clause-level alignments . they show that systems designed for word- or short phrase-based alignment are ill-suited for longer alignments. |
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Align and Augment: Generative Data Augmentation for Compositional Generalization (2024.eacl-long)
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| Challenge: | Recent work on semantic parsing has shown that seq2seq models find compositional generalization challenging. |
| Approach: | They propose a data-augmentation strategy that exploits alignment annotations between sentences and their corresponding meaning representations to improve compositional generalization. |
| Outcome: | The proposed model improves compositional generalization performance by exploiting alignment annotations between sentences and their corresponding meaning representations. |
Towards Structure-aware Paraphrase Identification with Phrase Alignment Using Sentence Encoders (2022.coling-1)
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| Challenge: | Existing paraphrase identification datasets exhibit high correlation between positive pairs and the degree of their lexical overlap. |
| Approach: | They propose to combine sentence encoders with an alignment component by representing each sentence as a list of predicate-argument spans and decomposing the sentence-level meaning comparison into the alignment between their spans. |
| Outcome: | The proposed approach improves performance and interpretability for various sentence encoders. |
Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
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| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
| Approach: | They propose a taxonomy of methods to improve cross-lingual alignment . they argue that an effective trade-off between language-neutral and language-specific information is key . |
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Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features (N18-1)
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| Challenge: | Currently, unsupervised word embeddings are routinely trained on large amounts of raw text data. |
| Approach: | They propose to use unsupervised word embeddings to train distributed representations of sentences. |
| Outcome: | The proposed method outperforms state-of-the-art models on most benchmark tasks and is robust to the produced general-purpose sentence embeddings. |
Compositional Generalization by Factorizing Alignment and Translation (2020.acl-srw)
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| Challenge: | a crucial property underlying the expressive power of human language is its systematicity. |
| Approach: | They propose to make an analogous separation between alignment and translation in neural machine translation to capture compositional structure. |
| Outcome: | The proposed architecture outperforms existing neural networks on a compositional generalization task without supervision. |
Composition-contrastive Learning for Sentence Embeddings (2023.acl-long)
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| Challenge: | Recent work shows potential to learn vector representations from unlabelled data without task-specific fine-tuning. |
| Approach: | They propose to maximize alignment between textual embeddings and a composition of their phrasal constituents. |
| Outcome: | The proposed approach improves on similarity tasks comparable to state-of-the-art approaches. |
Learning High-Quality and General-Purpose Phrase Representations (2024.findings-eacl)
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| Challenge: | Pre-trained language models for phrasal embeddings are unnecessarily complex and require to be pre-tuned on a corpus with context sentences. |
| Approach: | They propose a framework to learn phrase representations in a context-free fashion. |
| Outcome: | The proposed framework generates superior phrase embeddings while requiring a smaller model size. |
Are representations built from the ground up? An empirical examination of local composition in language models (2022.emnlp-main)
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| Challenge: | Compositionality is a hallmark of human language, but many phrases are non-compositional . a study by a team of researchers shows that LMs may not be able to distinguish between compositional and non-composable phrases. |
| Approach: | They propose to predict LM-internal representations of longer phrases given their constituents . they find that the representation of a parent phrase can be predicted with some accuracy . |
| Outcome: | The proposed model can predict a parent phrase with some accuracy given its children's transformations, but this is not the case. |