Papers by Nina Poerner
Interpretable Question Answering on Knowledge Bases and Text (P19-1)
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| Challenge: | Existing evaluation paradigms for ML based question answering models are lacking . a lack of explanation methods has been proposed for QA models . |
| Approach: | They propose an automatic evaluation paradigm for explanation methods in ML based question answering models . they adapt post hoc explanation methods such as LIME and input perturbation to the model . |
| Outcome: | The proposed evaluation paradigm compares explanation methods with human annotations. |
A Web Service for Pre-segmenting Very Long Transcribed Speech Recordings (L18-1)
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| Challenge: | a new algorithm that pre-segments long speech recordings into manageable chunks is proposed . the run time of classical text-to-speech alignment algorithms is quadratically growing with the length of the input . |
| Approach: | They propose two algorithms that pre-segment long speech recordings into manageable chunks . first algorithm is fast but cannot guarantee short chunks on noisy recordings or erroneous transcriptions a second algorithm delivers short chunk but is less effective in terms of run time and chunk boundary accuracy . |
| Outcome: | The proposed algorithms reduce the run time of the speech segmentation system to under real-time even on recordings that could not previously be processed. |
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT (2020.findings-emnlp)
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| Challenge: | Existing methods to enhance BERT with factual knowledge about entities require no additional pretraining and no changes to the encoder itself. |
| Approach: | They propose a way to inject factual knowledge into the pretrained BERT model by aligning Wikipedia2Vec entity vectors with BERT's native wordpiece vector space and feeding the aligned entity vector into BERT as if they were wordpieces. |
| Outcome: | The proposed version outperforms baseline models on unsupervised question answering, supervised relation classification and entity linking tasks. |
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity (2020.acl-main)
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| Challenge: | Existing word embeddings combine complementary strengths of their components to achieve unsupervised semantic similarity (STS). |
| Approach: | They propose to ensemble pre-trained sentence encoders into sentence meta-embeddings to achieve unsupervised Semantic Textual Similarity (STS) they adapt dimensionality reduction, generalized Canonical Correlation Analysis and cross-view auto-encoders to their work. |
| Outcome: | The proposed method achieves 3.7% to 6.4% Pearson’s r over single-source word embeddings on the STS Benchmark and on the StS12-STS16 datasets. |
Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA (2020.findings-emnlp)
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| Challenge: | Pretrained Language Models (PTLMs) are typically pretraining on target-domain text, which is expensive in terms of hardware, runtime and CO 2 emissions. |
| Approach: | They propose a faster, CPU-only domainadaptation method that trains Word2Vec on target-domain text and aligns the resulting word vectors with the wordpiece vectors of a general-domain PTLM. |
| Outcome: | The proposed method covers 60% of the BioBERT - BERT F1 delta, 5% of BioBERTS’s CO2 footprint and 2% of its cloud compute cost. |
Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection (D19-1)
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| Challenge: | Stack Exchange has fewer than 160 user-labeled duplicates, and 25% have fewer. |
| Approach: | They propose a framework that combines sentence encoders with unlabeled data to solve the problem of duplicate question detection in Community Question Answering forums. |
| Outcome: | The proposed framework outperforms BM25, a single-view system and a supervised domain-adversarial DQD method on the CQADupStack corpus and on Stack Exchange forums. |
Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement (P18-1)
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| Challenge: | a number of post hoc explanation methods for deep neural networks have been proposed . due to the complexity of the DNNs they explain, these methods are necessarily approximations and come with their own sources of error. |
| Approach: | They propose two evaluation paradigms that cover two important classes of NLP problems . they propose LIMSSE, LRP and DeepLIFT as the most effective explanation methods . |
| Outcome: | The proposed methods are most effective for explaining deep neural networks in NLP . the proposed methods can explain complex models without manual annotation . |