Search Results for author: Siva Reddy

Found 50 papers, 28 papers with code

Mind the Context: The Impact of Contextualization in Neural Module Networks for Grounding Visual Referring Expressions

no code implementations EMNLP 2021 Arjun Akula, Spandana Gella, Keze Wang, Song-Chun Zhu, Siva Reddy

Our model outperforms the state-of-the-art NMN model on CLEVR-Ref+ dataset with +8. 1% improvement in accuracy on the single-referent test set and +4. 3% on the full test set.

FaithDial: A Faithful Benchmark for Information-Seeking Dialogue

1 code implementation22 Apr 2022 Nouha Dziri, Ehsan Kamalloo, Sivan Milton, Osmar Zaiane, Mo Yu, Edoardo M. Ponti, Siva Reddy

To mitigate this behavior, we adopt a data-centric solution and create FaithDial, a new benchmark for hallucination-free dialogues, by editing hallucinated responses in the Wizard of Wikipedia (WoW) benchmark.

Dialogue Generation

On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?

1 code implementation17 Apr 2022 Nouha Dziri, Sivan Milton, Mo Yu, Osmar Zaiane, Siva Reddy

Knowledge-grounded conversational models are known to suffer from producing factually invalid statements, a phenomenon commonly called hallucination.

Image Retrieval from Contextual Descriptions

1 code implementation ACL 2022 Benno Krojer, Vaibhav Adlakha, Vibhav Vineet, Yash Goyal, Edoardo Ponti, Siva Reddy

In particular, models are tasked with retrieving the correct image from a set of 10 minimally contrastive candidates based on a contextual description.

Image Retrieval

Combining Modular Skills in Multitask Learning

1 code implementation28 Feb 2022 Edoardo M. Ponti, Alessandro Sordoni, Yoshua Bengio, Siva Reddy

By jointly learning these and a task-skill allocation matrix, the network for each task is instantiated as the average of the parameters of active skills.

reinforcement-learning

Does entity abstraction help generative Transformers reason?

no code implementations5 Jan 2022 Nicolas Gontier, Siva Reddy, Christopher Pal

We study the utility of incorporating entity type abstractions into pre-trained Transformers and test these methods on four NLP tasks requiring different forms of logical reasoning: (1) compositional language understanding with text-based relational reasoning (CLUTRR), (2) abductive reasoning (ProofWriter), (3) multi-hop question answering (HotpotQA), and (4) conversational question answering (CoQA).

Conversational Question Answering Multi-hop Question Answering +1

The Power of Prompt Tuning for Low-Resource Semantic Parsing

no code implementations ACL 2022 Nathan Schucher, Siva Reddy, Harm de Vries

Prompt tuning has recently emerged as an effective method for adapting pre-trained language models to a number of language understanding and generation tasks.

Semantic Parsing

Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining

1 code implementation15 Oct 2021 Andreas Madsen, Nicholas Meade, Vaibhav Adlakha, Siva Reddy

In this work, we adapt and improve a recently proposed faithfulness benchmark from computer vision called ROAR (RemOve And Retrain), by Hooker et al. (2019).

Compositional Generalization in Dependency Parsing

no code implementations ACL 2022 Emily Goodwin, Siva Reddy, Timothy J. O'Donnell, Dzmitry Bahdanau

To test compositional generalization in semantic parsing, Keysers et al. (2020) introduced Compositional Freebase Queries (CFQ).

Dependency Parsing Semantic Parsing

Post-hoc Interpretability for Neural NLP: A Survey

no code implementations10 Aug 2021 Andreas Madsen, Siva Reddy, Sarath Chandar

Neural networks for NLP are becoming increasingly complex and widespread, and there is a growing concern if these models are responsible to use.

Modelling Latent Translations for Cross-Lingual Transfer

1 code implementation23 Jul 2021 Edoardo Maria Ponti, Julia Kreutzer, Ivan Vulić, Siva Reddy

To remedy this, we propose a new technique that integrates both steps of the traditional pipeline (translation and classification) into a single model, by treating the intermediate translations as a latent random variable.

Cross-Lingual Transfer Few-Shot Learning +4

Minimax and Neyman-Pearson Meta-Learning for Outlier Languages

1 code implementation2 Jun 2021 Edoardo Maria Ponti, Rahul Aralikatte, Disha Shrivastava, Siva Reddy, Anders Søgaard

In fact, under a decision-theoretic framework, MAML can be interpreted as minimising the expected risk across training languages (with a uniform prior), which is known as Bayes criterion.

Meta-Learning Part-Of-Speech Tagging +1

Understanding by Understanding Not: Modeling Negation in Language Models

1 code implementation NAACL 2021 Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R Devon Hjelm, Alessandro Sordoni, Aaron Courville

To improve language models in this regard, we propose to augment the language modeling objective with an unlikelihood objective that is based on negated generic sentences from a raw text corpus.

Language Modelling

MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining

1 code implementation EMNLP (ClinicalNLP) 2020 Zhi Wen, Xing Han Lu, Siva Reddy

One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets.

 Ranked #1 on Mortality Prediction on MIMIC-III (Accuracy metric)

Mortality Prediction Natural Language Understanding

Words aren't enough, their order matters: On the Robustness of Grounding Visual Referring Expressions

1 code implementation ACL 2020 Arjun R. Akula, Spandana Gella, Yaser Al-Onaizan, Song-Chun Zhu, Siva Reddy

To measure the true progress of existing models, we split the test set into two sets, one which requires reasoning on linguistic structure and the other which doesn't.

Contrastive Learning Multi-Task Learning +2

Building a Neural Semantic Parser from a Domain Ontology

no code implementations25 Dec 2018 Jianpeng Cheng, Siva Reddy, Mirella Lapata

We address these challenges with a framework which allows to elicit training data from a domain ontology and bootstrap a neural parser which recursively builds derivations of logical forms.

Semantic Parsing

Learning Typed Entailment Graphs with Global Soft Constraints

1 code implementation TACL 2018 Mohammad Javad Hosseini, Nathanael Chambers, Siva Reddy, Xavier R. Holt, Shay B. Cohen, Mark Johnson, Mark Steedman

We instead propose a scalable method that learns globally consistent similarity scores based on new soft constraints that consider both the structures across typed entailment graphs and inside each graph.

Graph Learning

Learning an Executable Neural Semantic Parser

no code implementations CL 2019 Jianpeng Cheng, Siva Reddy, Vijay Saraswat, Mirella Lapata

This paper describes a neural semantic parser that maps natural language utterances onto logical forms which can be executed against a task-specific environment, such as a knowledge base or a database, to produce a response.

Learning to Paraphrase for Question Answering

no code implementations EMNLP 2017 Li Dong, Jonathan Mallinson, Siva Reddy, Mirella Lapata

Question answering (QA) systems are sensitive to the many different ways natural language expresses the same information need.

Question Answering

Learning Structured Natural Language Representations for Semantic Parsing

1 code implementation ACL 2017 Jianpeng Cheng, Siva Reddy, Vijay Saraswat, Mirella Lapata

We introduce a neural semantic parser that converts natural language utterances to intermediate representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains.

Semantic Parsing

Universal Dependencies to Logical Form with Negation Scope

no code implementations WS 2017 Federico Fancellu, Siva Reddy, Adam Lopez, Bonnie Webber

Many language technology applications would benefit from the ability to represent negation and its scope on top of widely-used linguistic resources.

Universal Semantic Parsing

1 code implementation EMNLP 2017 Siva Reddy, Oscar Täckström, Slav Petrov, Mark Steedman, Mirella Lapata

In this work, we introduce UDepLambda, a semantic interface for UD, which maps natural language to logical forms in an almost language-independent fashion and can process dependency graphs.

Question Answering Semantic Parsing

Universal Dependencies to Logical Forms with Negation Scope

1 code implementation10 Feb 2017 Federico Fancellu, Siva Reddy, Adam Lopez, Bonnie Webber

Many language technology applications would benefit from the ability to represent negation and its scope on top of widely-used linguistic resources.

Predicting Target Language CCG Supertags Improves Neural Machine Translation

no code implementations WS 2017 Maria Nadejde, Siva Reddy, Rico Sennrich, Tomasz Dwojak, Marcin Junczys-Dowmunt, Philipp Koehn, Alexandra Birch

Our results on WMT data show that explicitly modeling target-syntax improves machine translation quality for German->English, a high-resource pair, and for Romanian->English, a low-resource pair and also several syntactic phenomena including prepositional phrase attachment.

Machine Translation Prepositional Phrase Attachment +1

Evaluating Induced CCG Parsers on Grounded Semantic Parsing

1 code implementation EMNLP 2016 Yonatan Bisk, Siva Reddy, John Blitzer, Julia Hockenmaier, Mark Steedman

We compare the effectiveness of four different syntactic CCG parsers for a semantic slot-filling task to explore how much syntactic supervision is required for downstream semantic analysis.

Semantic Parsing Slot Filling

DNN-based Speech Synthesis for Indian Languages from ASCII text

no code implementations18 Aug 2016 Srikanth Ronanki, Siva Reddy, Bajibabu Bollepalli, Simon King

These methods first convert the ASCII text to a phonetic script, and then learn a Deep Neural Network to synthesize speech from that.

Speech Synthesis Text-To-Speech Synthesis

Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing

no code implementations WS 2016 Shashi Narayan, Siva Reddy, Shay B. Cohen

One of the limitations of semantic parsing approaches to open-domain question answering is the lexicosyntactic gap between natural language questions and knowledge base entries -- there are many ways to ask a question, all with the same answer.

Open-Domain Question Answering Paraphrase Generation +1

Transforming Dependency Structures to Logical Forms for Semantic Parsing

1 code implementation TACL 2016 Siva Reddy, Oscar T{\"a}ckstr{\"o}m, Michael Collins, Tom Kwiatkowski, Dipanjan Das, Mark Steedman, Mirella Lapata

In contrast{---}partly due to the lack of a strong type system{---}dependency structures are easy to annotate and have become a widely used form of syntactic analysis for many languages.

Question Answering Semantic Parsing +1

Large-scale Semantic Parsing without Question-Answer Pairs

no code implementations TACL 2014 Siva Reddy, Mirella Lapata, Mark Steedman

In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs.

Graph Matching Semantic Parsing

Word Sketches for Turkish

no code implementations LREC 2012 Bharat Ram Ambati, Siva Reddy, Adam Kilgarriff

Word sketches are one-page, automatic, corpus-based summaries of a word's grammatical and collocational behaviour.

Dependency Parsing Language Modelling

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