Search Results for author:
Found 23 papers, 6 papers with code
PEACH: Pre-Training Sequence-to-Sequence Multilingual Models for Translation with Semi-Supervised Pseudo-Parallel Document Generation
Multilingual pre-training on monolingual data ignores the availability of parallel data in many language pairs.
Generative Adversarial Training Can Improve Neural Language Models
While deep learning in the form of recurrent neural networks (RNNs) has caused a significant improvement in neural language modeling, the fact that they are extremely prone to overfitting is still a mainly unresolved issue.
$Λ$-DARTS: Mitigating Performance Collapse by Harmonizing Operation Selection among Cells
The main shortcoming of DARTS is performance collapse, where the discovered architecture suffers from a pattern of declining quality during search.
ARMAN: Pre-training with Semantically Selecting and Reordering of Sentences for Persian Abstractive Summarization
Current pre-training works in abstractive summarization give more points to the summaries with more words in common with the main text and pay less attention to the semantic similarity between generated sentences and the original document.
NLP-IIS@UT at SemEval-2021 Task 4: Machine Reading Comprehension using the Long Document Transformer
This paper presents a technical report of our submission to the 4th task of SemEval-2021, titled: Reading Comprehension of Abstract Meaning.
UTNLP at SemEval-2021 Task 5: A Comparative Analysis of Toxic Span Detection using Attention-based, Named Entity Recognition, and Ensemble Models
Detecting which parts of a sentence contribute to that sentence's toxicity -- rather than providing a sentence-level verdict of hatefulness -- would increase the interpretability of models and allow human moderators to better understand the outputs of the system.
Distilling Knowledge for Fast Retrieval-based Chat-bots
Response retrieval is a subset of neural ranking in which a model selects a suitable response from a set of candidates given a conversation history.
Enriching Conversation Context in Retrieval-based Chatbots
Recent work has shown that these language models can be used in text-matching scenarios to create Bi-encoders that perform almost as well as Cross-encoders while having a much faster inference speed.
Online News Media Website Ranking Using User Generated Content
The use of user-generated content in this framework, as a partly-unbiased, real-time and low cost content on the web distinguishes the proposed news website ranking framework from the literature.
Newswire versus Social Media for Disaster Response and Recovery
In a disaster situation, first responders need to quickly acquire situational awareness and prioritize response based on the need, resources available and impact.
Cross-lingual Subjectivity Detection for Resource Lean Languages
Wide and universal changes in the web content due to the growth of web 2 applications increase the importance of user-generated content on the web.
Emad at SemEval-2019 Task 6: Offensive Language Identification using Traditional Machine Learning and Deep Learning approaches
In this paper, the used methods and the results obtained by our team, entitled Emad, on the OffensEval 2019 shared task organized at SemEval 2019 are presented.
LICD: A Language-Independent Approach for Aspect Category Detection
For a given sentence, our proposed method performs ACD based on two hypotheses: First, a category should be assigned to a sentence if there is a high semantic similarity between the sentence and a set of representative words of that category.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+6
Aspect Category Detection via Topic-Attention Network
In this paper, we propose a deep neural network method based on attention mechanism to identify different aspect categories of a given review sentence.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
An Unsupervised Approach for Aspect Category Detection Using Soft Cosine Similarity Measure
Besides, most of these supervised methods require feature engineering to perform well.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Deep Neural Networks for Query Expansion using Word Embeddings
In this paper, we show that this is also true for more recently proposed embedding-based query expansion methods.
PEYMA: A Tagged Corpus for Persian Named Entities
The goal in the NER task is to classify proper nouns of a text into classes such as person, location, and organization.
Learning to Weight Translations using Ordinal Linear Regression and Query-generated Training Data for Ad-hoc Retrieval with Long Queries
To this end, first we build a query-generated training data using pseudo-relevant documents to the query and all translation candidates.
Dimension Projection among Languages based on Pseudo-relevant Documents for Query Translation
Using top-ranked documents in response to a query has been shown to be an effective approach to improve the quality of query translation in dictionary-based cross-language information retrieval.
SS4MCT: A Statistical Stemmer for Morphologically Complex Texts
Stemming is a common approach to this end.
Regression and Learning to Rank Aggregation for User Engagement Evaluation
In this paper, we consider a tweet containing a rating for a movie as an instance and focus on ranking the instances of each user based on their engagement, i. e., the total number of retweets and favorites it will gain.
A Probabilistic Translation Method for Dictionary-based Cross-lingual Information Retrieval in Agglutinative Languages
Translation ambiguity, out of vocabulary words and missing some translations in bilingual dictionaries make dictionary-based Cross-language Information Retrieval (CLIR) a challenging task.
Learning to Exploit Different Translation Resources for Cross Language Information Retrieval
To evaluate the proposed method we do English-Persian CLIR, in which we employ the translation ranking model to find translations of English queries and employ the translations to retrieve Persian documents.
