Pushpendre Rastogi


Scaling Multi-Domain Dialogue State Tracking via Query Reformulation
Pushpendre Rastogi | Arpit Gupta | Tongfei Chen | Mathias Lambert
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)

We present a novel approach to dialogue state tracking and referring expression resolution tasks. Successful contextual understanding of multi-turn spoken dialogues requires resolving referring expressions across turns and tracking the entities relevant to the conversation across turns. Tracking conversational state is particularly challenging in a multi-domain scenario when there exist multiple spoken language understanding (SLU) sub-systems, and each SLU sub-system operates on its domain-specific meaning representation. While previous approaches have addressed the disparate schema issue by learning candidate transformations of the meaning representation, in this paper, we instead model the reference resolution as a dialogue context-aware user query reformulation task – the dialog state is serialized to a sequence of natural language tokens representing the conversation. We develop our model for query reformulation using a pointer-generator network and a novel multi-task learning setup. In our experiments, we show a significant improvement in absolute F1 on an internal as well as a, soon to be released, public benchmark respectively.

Improving Long Distance Slot Carryover in Spoken Dialogue Systems
Tongfei Chen | Chetan Naik | Hua He | Pushpendre Rastogi | Lambert Mathias
Proceedings of the First Workshop on NLP for Conversational AI

Tracking the state of the conversation is a central component in task-oriented spoken dialogue systems. One such approach for tracking the dialogue state is slot carryover, where a model makes a binary decision if a slot from the context is relevant to the current turn. Previous work on the slot carryover task used models that made independent decisions for each slot. A close analysis of the results show that this approach results in poor performance over longer context dialogues. In this paper, we propose to jointly model the slots. We propose two neural network architectures, one based on pointer networks that incorporate slot ordering information, and the other based on transformer networks that uses self attention mechanism to model the slot interdependencies. Our experiments on an internal dialogue benchmark dataset and on the public DSTC2 dataset demonstrate that our proposed models are able to resolve longer distance slot references and are able to achieve competitive performance.


Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework
Aaron Steven White | Pushpendre Rastogi | Kevin Duh | Benjamin Van Durme
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We propose to unify a variety of existing semantic classification tasks, such as semantic role labeling, anaphora resolution, and paraphrase detection, under the heading of Recognizing Textual Entailment (RTE). We present a general strategy to automatically generate one or more sentential hypotheses based on an input sentence and pre-existing manual semantic annotations. The resulting suite of datasets enables us to probe a statistical RTE model’s performance on different aspects of semantics. We demonstrate the value of this approach by investigating the behavior of a popular neural network RTE model.

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CADET: Computer Assisted Discovery Extraction and Translation
Benjamin Van Durme | Tom Lippincott | Kevin Duh | Deana Burchfield | Adam Poliak | Cash Costello | Tim Finin | Scott Miller | James Mayfield | Philipp Koehn | Craig Harman | Dawn Lawrie | Chandler May | Max Thomas | Annabelle Carrell | Julianne Chaloux | Tongfei Chen | Alex Comerford | Mark Dredze | Benjamin Glass | Shudong Hao | Patrick Martin | Pushpendre Rastogi | Rashmi Sankepally | Travis Wolfe | Ying-Ying Tran | Ted Zhang
Proceedings of the IJCNLP 2017, System Demonstrations

Computer Assisted Discovery Extraction and Translation (CADET) is a workbench for helping knowledge workers find, label, and translate documents of interest. It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users. This open-source framework allows for easy development of new research prototypes using a micro-service architecture based atop Docker and Apache Thrift.

Efficient, Compositional, Order-sensitive n-gram Embeddings
Adam Poliak | Pushpendre Rastogi | M. Patrick Martin | Benjamin Van Durme
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We propose ECO: a new way to generate embeddings for phrases that is Efficient, Compositional, and Order-sensitive. Our method creates decompositional embeddings for words offline and combines them to create new embeddings for phrases in real time. Unlike other approaches, ECO can create embeddings for phrases not seen during training. We evaluate ECO on supervised and unsupervised tasks and demonstrate that creating phrase embeddings that are sensitive to word order can help downstream tasks.


Weighting Finite-State Transductions With Neural Context
Pushpendre Rastogi | Ryan Cotterell | Jason Eisner
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Problems With Evaluation of Word Embeddings Using Word Similarity Tasks
Manaal Faruqui | Yulia Tsvetkov | Pushpendre Rastogi | Chris Dyer
Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP


Script Induction as Language Modeling
Rachel Rudinger | Pushpendre Rastogi | Francis Ferraro | Benjamin Van Durme
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

Multiview LSA: Representation Learning via Generalized CCA
Pushpendre Rastogi | Benjamin Van Durme | Raman Arora
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

FrameNet+: Fast Paraphrastic Tripling of FrameNet
Ellie Pavlick | Travis Wolfe | Pushpendre Rastogi | Chris Callison-Burch | Mark Dredze | Benjamin Van Durme
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification
Ellie Pavlick | Pushpendre Rastogi | Juri Ganitkevitch | Benjamin Van Durme | Chris Callison-Burch
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)


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Augmenting FrameNet Via PPDB
Pushpendre Rastogi | Benjamin Van Durme
Proceedings of the Second Workshop on EVENTS: Definition, Detection, Coreference, and Representation

A Wikipedia-based Corpus for Contextualized Machine Translation
Jennifer Drexler | Pushpendre Rastogi | Jacqueline Aguilar | Benjamin Van Durme | Matt Post
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We describe a corpus for target-contextualized machine translation (MT), where the task is to improve the translation of source documents using language models built over presumably related documents in the target language. The idea presumes a situation where most of the information about a topic is in a foreign language, yet some related target-language information is known to exist. Our corpus comprises a set of curated English Wikipedia articles describing news events, along with (i) their Spanish counterparts and (ii) some of the Spanish source articles cited within them. In experiments, we translated these Spanish documents, treating the English articles as target-side context, and evaluate the effect on translation quality when including target-side language models built over this English context and interpolated with other, separately-derived language model data. We find that even under this simplistic baseline approach, we achieve significant improvements as measured by BLEU score.