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Anusha Balakrishnan

Anusha Balakrishnan has 2 papers in our library, in 2018–2019, most of them on Dialogue systems, Discourse analysis, Text generation; often with Derek Chen, He He, Jinfeng Rao.

2 papers in our library · 2018–2019

OpenAlex · h-index 6 · 13 works · 493 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 179 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #3889

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢MetaMenlo Park · United States2019 · 4 paperscurrent🎓Carnegie Mellon UniversityPittsburgh · United States2019 · 2 paperscurrent🎓The Ohio State UniversityColumbus · United States2019 · 2 paperscurrent🎓Stanford UniversityStanford · United States2014–2018 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 3Research subjects and languages the work is about.Dialogue systems · 2 worksDiscourse analysis · 1 workText generation · 1 work

 Works by year

2010s

20191 paperConstrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue20181 paperDecoupling Strategy and Generation in Negotiation Dialogues

 Papers

The most cited first (OpenAlex and our library).

  1. Decoupling Strategy and Generation in Negotiation Dialogues (2018 · 233 citations)
    End-to-end neural negotiation agents learn strategy and wording together. This makes their behaviour hard to steer or inspect, and reinforcement learning tends to collapse their language into repetitive or ungrammatical output. The authors separate the two concerns with a three-part pipeline:
  2. Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue (2019 · 132 citations)
    Neural sequence-to-sequence generators for task-oriented dialogue usually take flat slot-value meaning representations (MRs) as input.

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Decoupling Strategy and Generation in Negotiation DialoguesConference on Empirical Methods in Natural Language Processing (EMNLP)He He, Derek Chen, Anusha Balakrishnan, et al.2018 · 233 citations
  2. Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented DialogueAnnual Meeting of the Association for Computational Linguistics (ACL)Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, et al.2019 · 132 citations
  3. A Tree-to-Sequence Model for Neural NLG in Task-Oriented DialogProceedings of the 12th International Conference on Natural Language Generation · original ↗ · no summary yetJinfeng Rao et al.2019 · 27 citations
  4. The OSU/Facebook Realizer for SRST 2019: Seq2Seq Inflection and Serialized Tree2Tree LinearizationProceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019) · original ↗ · no summary yetKartikeya Upasani et al.2019

 Co-authors here

7 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 179 times (of the 141,840 papers whose bibliographies we read); 18 citing papers with a page here →.

Updated

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