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Query Focused Multi-document Summarisation of Biomedical Texts
[article]
2020
arXiv
pre-print
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Our overall framework implements Query focused multi-document extractive summarisation by applying either a classification or a regression layer to the candidate sentence embeddings and to the comparison ...
We observe the best results when BERT is used to obtain the word embeddings, followed by an LSTM layer to obtain sentence embeddings. ...
Acknowledgements Research by Vincent Nguyen is supported by the Australian Research Training Program and the CSIRO Postgraduate Scholarship. ...
arXiv:2008.11986v1
fatcat:2byseq2senhc7ezlojpot3z2bq
On the impressive performance of randomly weighted encoders in summarization tasks
[article]
2020
arXiv
pre-print
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We hypothesize that random projections of an input text have enough representational power to encode the hierarchical structure of sentences and semantics of documents. ...
Using a trained decoder to produce abstractive text summaries, we empirically demonstrate that architectures with untrained randomly initialized encoders perform competitively with respect to the equivalent ...
This work was partially supported by the IVADO Excellence Scholarship and the Canada First Research Excellence Fund. ...
arXiv:2002.09084v1
fatcat:kra4gsrabjhxvcmbqi2mmnbzdi
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
[article]
2018
arXiv
pre-print
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"Downstream" tasks, often based on sentence classification, are commonly used to evaluate the quality of sentence representations. ...
We introduce here 10 probing tasks designed to capture simple linguistic features of sentences, and we use them to study embeddings generated by three different encoders trained in eight distinct ways, ...
Classification performed by a MLP with sigmoid nonlinearity, taking pre-learned sentence embeddings as input (see Appendix for details and logistic regression results). ...
arXiv:1805.01070v2
fatcat:dfhb2n4vojczrg7kziykkmguze
More Than Words: Towards Better Quality Interpretations of Text Classifiers
[article]
2021
arXiv
pre-print
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These issues have led to the adoption of methods like SHAP and Integrated Gradients to explain classification decisions by assigning importance scores to input tokens. ...
The large size and complex decision mechanisms of state-of-the-art text classifiers make it difficult for humans to understand their predictions, leading to a potential lack of trust by the users. ...
up new benchmarks for text classification. ...
arXiv:2112.12444v1
fatcat:p7glpcebxfbufnscwtfomrwqiq
Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks
[article]
2018
arXiv
pre-print
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In this paper, we propose a new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts. ...
Despite its usefulness for this task, most current approaches to argument mining are designed for use only with specific text types and fall short when applied to heterogeneous texts. ...
applied by untrained annotators. ...
arXiv:1802.05758v1
fatcat:wg7joeh2tndonirmmiwupm23iu
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
2018
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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"Downstream" tasks, often based on sentence classification, are commonly used to evaluate the quality of sentence representations. ...
We introduce here 10 probing tasks designed to capture simple linguistic features of sentences, and we use them to study embeddings generated by three different encoders trained in eight distinct ways, ...
Classification performed by a MLP with sigmoid nonlinearity, taking pre-learned sentence embeddings as input (see Appendix for details and logistic regression results). ...
doi:10.18653/v1/p18-1198
dblp:conf/acl/BaroniBLKC18
fatcat:tqjfd266snfyngrftmmvse2qce
Term Extraction via Neural Sequence Labeling a Comparative Evaluation of Strategies Using Recurrent Neural Networks
2018
Interspeech 2018
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To do so we have worked with different kinds of recurrent neural networks and word embeddings. ...
describe how one can built a state-of-theart term extraction systems with this single-stage technique and compare different network types and topologies and also examine the influence of the type of input embedding ...
These embeddings are usually trained by building a classificator for an auxillary classification task, such as skip-grams [16] . ...
doi:10.21437/interspeech.2018-2017
dblp:conf/interspeech/KuczaNZWS18
fatcat:7lucz7dbvvfqvh6n6derbvs2aq
Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis
[article]
2019
arXiv
pre-print
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We make a fair comparison between the tasks by holding constant the quantity and genre of the training data, as well as the LSTM architecture. ...
each sentence in a running text. ...
sentence-level classification tasks. ...
arXiv:1809.10040v2
fatcat:prdxahnek5dyxnej3kcj2exyii
Discourse Coherence in the Wild: A Dataset, Evaluation and Methods
[article]
2018
arXiv
pre-print
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We analyze these performance differences and discuss patterns we observed in low coherence texts in four domains. ...
To address this, we present a new corpus of real-world texts (GCDC) as well as the first large-scale evaluation of leading discourse coherence algorithms. ...
Minority Class Classification One application of a coherence classification system would be to provide feedback to writers by flagging text that is not very coherent. ...
arXiv:1805.04993v1
fatcat:2heidfd265eihngdqpuwz33nvy
Discourse Coherence in the Wild: A Dataset, Evaluation and Methods
2018
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
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We analyze these performance differences and discuss patterns we observed in low coherence texts in four domains. ...
To address this, we present a new corpus of real-world texts (GCDC) as well as the first large-scale evaluation of leading discourse coherence algorithms. ...
Minority Class Classification One application of a coherence classification system would be to provide feedback to writers by flagging text that is not very coherent. ...
doi:10.18653/v1/w18-5023
dblp:conf/sigdial/LaiT18
fatcat:etcfk3hs5vfozc62ul557gmthm
Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence
[article]
2021
arXiv
pre-print
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Given the sentence embeddings, the upper-level transformer is trained to recover the segmentation boundaries as well as the topic labels of each sentence. ...
It consists of two components: bottom-level sentence encoders using pre-trained transformers, and an upper-level transformer-based segmentation model based on the sentence embeddings. ...
Sentence Classification at the Upper Level Once the sentence embeddings are obtained, we train a transformer model at the upper level of the architecture to classify 1) whether each sentence is the segment ...
arXiv:2110.07160v1
fatcat:hmrqbe4i3ncg7nxgkaplnrhbqq
Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models
[article]
2020
arXiv
pre-print
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Models such as ViLBERT, LXMERT and UNITER have significantly lifted state of the art across a wide range of V+L benchmarks with joint image-text pre-training. ...
decipher the inner workings of multimodal pre-training (e.g., the implicit knowledge garnered in individual attention heads, the inherent cross-modal alignment learned through contextualized multimodal embeddings ...
Inspired by BERT [9] , a common practice for pre-training V+L models is to first encode image regions and sentence words into a common embedding space, then use multiple Transformer layers to learn image-text ...
arXiv:2005.07310v2
fatcat:bzpkniisubggzgtxlskstyh47a
BERT-Based Sentiment Analysis: A Software Engineering Perspective
[article]
2021
arXiv
pre-print
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., BERT, RoBERTa, ALBERT, etc.) have displayed better results in the text classification task. ...
Following this context, the present research explores different BERT-based models to analyze the sentences in GitHub comments, Jira comments, and Stack Overflow posts. ...
Pagliardini, M., Gupta, P., Jaggi, M.: Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features. ...
arXiv:2106.02581v3
fatcat:grp55d3j3zbf3pg7ri2urechuu
Text Ranking and Classification using Data Compression
[article]
2021
arXiv
pre-print
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Text affinity scores derived from compressed sizes can be used for classification and ranking tasks, but their success depends on the compression tools used. ...
A well-known but rarely used approach to text categorization uses conditional entropy estimates computed using data compression tools. ...
Halford, "Text classification by data compression," 2021, https://maxhalford.github. ...
arXiv:2109.11577v2
fatcat:l2ffdj6pyfdozbav23plhs6fri
Fine-grained Sentiment Analysis with Faithful Attention
[article]
2019
arXiv
pre-print
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While the general task of textual sentiment classification has been widely studied, much less research looks specifically at sentiment between a specified source and target. ...
Thus, we directly trained the model's attention with human rationales and improved our model performance by a robust 4~8 points on all tasks we defined on our data sets. ...
The final sentence representation used for classification is then a weighted sum, z = n i=1Â (i)h i , to be fed into a fully connected layer with softmax activation for classification; i.e. y = sof tmax ...
arXiv:1908.06870v1
fatcat:4whgfjuw25codjv3ttpd27rhrq
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