Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics
Improving Medical NLI Using Context-Aware Domain Knowledge (2020.starsem-1)
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| Challenge: | Domain knowledge is important to understand both the lexical and relational associations of words in natural language text . lack of annotated dataset can lead to wrong inference predictions . |
| Approach: | They propose a knowledge adaptive approach that encodes the premise/hypothesis texts by leveraging supplementary external knowledge alongside the UMLS based on the word contexts. |
| Outcome: | The proposed model can align token-level interactions between the premise and hypothesis more effectively. |
Reading Comprehension as Natural Language Inference:A Semantic Analysis (2020.starsem-1)
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Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Li, Pavan Kapanipathi, Kartik Talamadupula
| Challenge: | In recent past, Natural language Inference (NLI) has gained significant attention, but its true impact has not been well studied. |
| Approach: | They propose to transform a large RACE dataset into an NLI model and compare it to a state-of-the-art model. |
| Outcome: | The proposed model outperforms the previous model on a question-answer concatenation form and a coherent entailment form. |
Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference (2020.starsem-1)
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| Challenge: | a logic-based approach to Natural Language Inference is becoming less and less common . a new method uses semantic relations to abduct sentences from data . |
| Approach: | They propose a method to reverse a theorem-proving procedure to abduct semantic relations from data. |
| Outcome: | The proposed method improves the performance of the theorem prover on the SICK dataset by 1.4% while maintaining high precision (>94%) |
Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings (2020.starsem-1)
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| Challenge: | Normative studies on modality for English words are relatively common . however, they are limited to a relatively small number of languages and require costly ratings. |
| Approach: | They aim to learn a mapping between word embeddings and modality norms by training on a high-resource language and testing on . monolingual and crosslingual word embeds are used to predict modality association scores . |
| Outcome: | The proposed model predicts modality associations even when trained on an English resource and tested on a completely unseen language. |
Joint Training for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora (2020.starsem-1)
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| Challenge: | Existing methods for learning cross-lingual word embeddings incorporate sub-word information during training. |
| Approach: | They propose a method that incorporates sub-word information during training to learn cross-lingual word embeddings from monolingual data and a bilingual lexicon. |
| Outcome: | The proposed method improves on bilingual lexicon induction, monolingual word similarity, and document classification using low-resource languages. |
Semantic Structural Decomposition for Neural Machine Translation (2020.starsem-1)
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| Challenge: | Existing methods for translation of long sentences are limited by the translation of single sentences to single sentences. |
| Approach: | They propose to use semantic splitting of the source sentence as preprocessing for machine translation. |
| Outcome: | The proposed approach tackles two main limitations of state-of-the-art machine translation. |
Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus Detection (2020.starsem-1)
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| Challenge: | Emotion stimulus detection is the task of finding the cause of an emotion in a textual description. |
| Approach: | They propose to evaluate whether clause classification or token sequence labeling is better for emotion stimulus detection in English. |
| Outcome: | The proposed framework compares clause classification and token sequence labeling on four English datasets. |
Knowledge Graphs meet Moral Values (2020.starsem-1)
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| Challenge: | Moral Foundations Theory (MFT) is one of the most adopted theories of morality due to its accompanying lexicon, the Moral Foundation Dictionary (MFD). |
| Approach: | They propose to use the Moral Foundation Dictionary to analyze moral values in three widely used KGs and propose several Personalized PageRank variations to score concepts and entities in the KG with respect to their relevance to the different moral values. |
| Outcome: | The proposed methods help to operationalize morality in both NLP and KG communities. |
Natural Language Inference with Mixed Effects (2020.starsem-1)
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| Challenge: | aggregating raw annotations to a single label is problematic due to disagreement among annotators. |
| Approach: | They propose a generic method that allows one to skip the aggregation step and train on the raw annotations directly without subjecting the model to unwanted noise. |
| Outcome: | The proposed method improves performance over models that do not incorporate such effects. |
On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT (2020.starsem-1)
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| Challenge: | Existing studies have found that BERT can correctly retrieve noun hypernyms in cloze tasks, but this does not correspond to systematic knowledge in BERT. |
| Approach: | They propose to use BERT to probe for hypernymy knowledge encoded in representations for cloze tasks to find out whether it is systematic or not . |
| Outcome: | The proposed model can retrieve hypernyms in cloze tasks, but not systematic knowledge in BERT. |
Topology of Word Embeddings: Singularities Reflect Polysemy (2020.starsem-1)
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| Challenge: | a new study suggests that word vectors live on a submanifold within their ambient vector space . a manifold hypothesis suggests that vectors should live on pinched manifels . |
| Approach: | They propose a topological measure of polysemy that correlates well with the actual number of meanings of a word. |
| Outcome: | The proposed method produces competitive results for a word Sense Induction & Disambiguation task. |
Assessing Polyseme Sense Similarity through Co-predication Acceptability and Contextualised Embedding Distance (2020.starsem-1)
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| Challenge: | Co-predication is a commonly used linguistic test to tell apart shifts in polysemic sense from changes in homonymic meaning. |
| Approach: | They examine how co-predication acceptability relates to explicit ratings of polyseme word sense similarity and how well they can be predicted through the distance between target words’ contextualised word embeddings. |
| Outcome: | The proposed measures can be predicted through the distance between target words’ contextualised word embeddings. |
Fine-tuning BERT with Focus Words for Explanation Regeneration (2020.starsem-1)
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| 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. |
PISA: A measure of Preference In Selection of Arguments to model verb argument recoverability (2020.starsem-1)
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| Challenge: | a computational model of the semantic recoverability of verb arguments is tested on direct objects and Instruments. |
| Approach: | They propose a computational model of the semantic recoverability of verb arguments . they use a selectional preference model to compute selectional preferences of verbs . |
| Outcome: | The proposed model can predict the recoverability of objects and Instruments at a much cheaper computational cost. |
Learning Negation Scope from Syntactic Structure (2020.starsem-1)
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| Challenge: | a semi-supervised model learns the semantics of negation purely through syntactic analysis . Negation is a semantic phenomenon in natural language which varies significantly . |
| Approach: | They propose a semi-supervised model which learns negation semantics purely through syntactic analysis. |
| Outcome: | The proposed model achieves state-of-the-art on a Negation Scope Detection task without identifying individual words or extracting features beyond syntax. |
A Visuospatial Dataset for Naturalistic Verb Learning (2020.starsem-1)
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| Challenge: | a new dataset is available for training and evaluating grounded language models . our data is designed to emulate the quality of language data a pre-verbal child would have access to . |
| Approach: | They propose a dataset for training and evaluating grounded language models . they use naturalistic, spontaneous speech paired with richly grounded visuospatial context . |
| Outcome: | The proposed dataset compares two distributional semantics models with one that does not. |
Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking (2020.starsem-1)
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| Challenge: | Existing methods for dialog state tracking are ontology-based and ontologie-free . however, it is not clear enough which slots are better handled by either of the two methods . |
| Approach: | They propose a dual-strategy model that integrates both ontology-based and ontological-free methods. |
| Outcome: | The proposed model outperforms the existing model on noisy and cleaner datasets. |
“where is this relationship going?”: Understanding Relationship Trajectories in Narrative Text (2020.starsem-1)
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| Challenge: | Existing models of human social relationships lack the syntactic and semantic signals needed to comprehend complex relationships. |
| Approach: | They propose two commonsense reasoning tasks to help people understand relationships . they use a dataset of 1250 stories documenting daily social interactions . |
| Outcome: | The proposed evaluation tasks parallel two real-life questions people frequently ponder upon as they navigate different social situations. |
Large Scale Author Obfuscation Using Siamese Variational Auto-Encoder: The SiamAO System (2020.starsem-1)
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| Challenge: | Existing approaches to author obfuscation are largely heuristic, but they can be used to attack author identification. |
| Approach: | They propose a deep learning architecture for constructing adversarial examples against similarity-based learners and explore its application to author obfuscation. |
| Outcome: | The proposed architectures show that they can be used to attack author obfuscation . the proposed architecture shows that it can be applied to obliquacy of text . |