Semantically Driven Sentence Fusion: Modeling and Evaluation

• URL: http://arxiv.org/abs/2010.02592v1
• Date: Tue, 6 Oct 2020 10:06:01 GMT
• Title: Semantically Driven Sentence Fusion: Modeling and Evaluation
• Authors: Eyal Ben-David, Orgad Keller, Eric Malmi, Idan Szpektor, Roi Reichart
• Abstract summary: Sentence fusion is the task of joining related sentences into coherent text. Current training and evaluation schemes for this task are based on single reference ground-truths. We show that this hinders models from robustly capturing the semantic relationship between input sentences.
• Score: 27.599227950466442
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sentence fusion is the task of joining related sentences into coherent text. Current training and evaluation schemes for this task are based on single reference ground-truths and do not account for valid fusion variants. We show that this hinders models from robustly capturing the semantic relationship between input sentences. To alleviate this, we present an approach in which ground-truth solutions are automatically expanded into multiple references via curated equivalence classes of connective phrases. We apply this method to a large-scale dataset and use the augmented dataset for both model training and evaluation. To improve the learning of semantic representation using multiple references, we enrich the model with auxiliary discourse classification tasks under a multi-tasking framework. Our experiments highlight the improvements of our approach over state-of-the-art models.

Related papers

• Fine-Tuning with Divergent Chains of Thought Boosts Reasoning Through Self-Correction in Language Models [63.36637269634553]
  We present a novel method of further improving performance by requiring models to compare multiple reasoning chains. We find that instruction tuning on DCoT datasets boosts the performance of even smaller, and therefore more accessible, language models.
  arXiv  Detail & Related papers  (2024-07-03T15:01:18Z)
• Preserving Modality Structure Improves Multi-Modal Learning [64.10085674834252]
  Self-supervised learning on large-scale multi-modal datasets allows learning semantically meaningful embeddings without relying on human annotations. These methods often struggle to generalize well on out-of-domain data as they ignore the semantic structure present in modality-specific embeddings. We propose a novel Semantic-Structure-Preserving Consistency approach to improve generalizability by preserving the modality-specific relationships in the joint embedding space.
  arXiv  Detail & Related papers  (2023-08-24T20:46:48Z)
• Conjunct Resolution in the Face of Verbal Omissions [51.220650412095665]
  We propose a conjunct resolution task that operates directly on the text and makes use of a split-and-rephrase paradigm in order to recover the missing elements in the coordination structure. We curate a large dataset, containing over 10K examples of naturally-occurring verbal omissions with crowd-sourced annotations. We train various neural baselines for this task, and show that while our best method obtains decent performance, it leaves ample space for improvement.
  arXiv  Detail & Related papers  (2023-05-26T08:44:02Z)
• Learning Data Representations with Joint Diffusion Models [20.25147743706431]
  Joint machine learning models that allow synthesizing and classifying data often offer uneven performance between those tasks or are unstable to train. We extend the vanilla diffusion model with a classifier that allows for stable joint end-to-end training with shared parameterization between those objectives. The resulting joint diffusion model outperforms recent state-of-the-art hybrid methods in terms of both classification and generation quality on all evaluated benchmarks.
  arXiv  Detail & Related papers  (2023-01-31T13:29:19Z)
• Class Enhancement Losses with Pseudo Labels for Zero-shot Semantic Segmentation [40.09476732999614]
  Mask proposal models have significantly improved the performance of zero-shot semantic segmentation. The use of a background' embedding during training in these methods is problematic as the resulting model tends to over-learn and assign all unseen classes as the background class instead of their correct labels. This paper proposes novel class enhancement losses to bypass the use of the background embbedding during training, and simultaneously exploit the semantic relationship between text embeddings and mask proposals by ranking the similarity scores.
  arXiv  Detail & Related papers  (2023-01-18T06:55:02Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• Relational Sentence Embedding for Flexible Semantic Matching [86.21393054423355]
  We present Sentence Embedding (RSE), a new paradigm to discover further the potential of sentence embeddings. RSE is effective and flexible in modeling sentence relations and outperforms a series of state-of-the-art embedding methods.
  arXiv  Detail & Related papers  (2022-12-17T05:25:17Z)
• VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification [3.7798600249187295]
  Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream task. In this paper, we approach the document classification problem by learning cross-modal representations through language and vision cues. The proposed method exploits high-level interactions and learns relevant semantic information from effective attention flows within and across modalities.
  arXiv  Detail & Related papers  (2022-05-24T12:28:12Z)
• Meeting Summarization with Pre-training and Clustering Methods [6.47783315109491]
  HMNetcitehmnet is a hierarchical network that employs both a word-level transformer and a turn-level transformer, as the baseline. We extend the locate-then-summarize approach of QMSumciteqmsum with an intermediate clustering step. We compare the performance of our baseline models with BART, a state-of-the-art language model that is effective for summarization.
  arXiv  Detail & Related papers  (2021-11-16T03:14:40Z)
• Dialogue Summarization with Supporting Utterance Flow Modeling and Fact Regularization [58.965859508695225]
  We propose an end-to-end neural model for dialogue summarization with two novel modules. The supporting utterance flow modeling helps to generate a coherent summary by smoothly shifting the focus from the former utterances to the later ones. The fact regularization encourages the generated summary to be factually consistent with the ground-truth summary during model training.
  arXiv  Detail & Related papers  (2021-08-03T03:09:25Z)
• VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling [24.775371434410328]
  We tackle the task of definition modeling, where the goal is to learn to generate definitions of words and phrases. Existing approaches for this task are discriminative, combining distributional and lexical semantics in an implicit rather than direct way. We propose a generative model for the task, introducing a continuous latent variable to explicitly model the underlying relationship between a phrase used within a context and its definition.
  arXiv  Detail & Related papers  (2020-10-07T02:48:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.