I joined Samaya AI, an early-stage startup, to build the next generation AI-powered knowledge-discovery platform. We leverage recent developments in LLMs (Large Language Models) to help domain experts answer complex questions over large sets of knowledge-intensive documents. I apply cutting-edge methods (including some of my prior work) to support such use cases and develop new ML techniques when existing methods are not good enough. I also enjoy applying learnings from Stanford's startup classes as I get to interview users and design solutions to address their pain points. We're a fun team of smart and kind people and we are always looking for amazing people (like you). Send me an email if you're interested.

Prior to Samaya, I graduated from Stanford University with a PhD in CS. I was advised by Prof. Christopher Manning in the NLP group. My research focus was broadly in open-domain dialogue systems and using retrieval to generate language. More specifically I created informative dialogue systems based on an understanding how humans talk informatively by training neural retrievers to find passages containing world knowledge and then paraphrasing it into conversational utterances. I also like to think about speech interfaces of the future, where humans and virtual agents take turns more naturally: with backchannels, interjections and clarification questions.

In 2020, I co-led Stanford's team in the Alexa Prize Socialbot competition. It was great fun leading a team of 10 people with weekly sprints, 1-1s, research discussions and a reading group. That was our first year participating and our bot Chirpy Cardinal that we built from the ground up, placed 2nda b c out of 10 teams! All of our code is open-sourced and here's a live demo. All the real-life issues that couldn't be fixed with existing methods became my research agenda. I was a research mentor for the next iteration and our team went on to publish 5 peer-reviewed conference papers and 2 technical articles (find them here).

Before the PhD, I got master's degree in CS(AI) at Stanford broadening my understanding with a variety of courses and conducting research with Prof. Jure Leskovec, Robert West and Austin Benson. For my bachelor's degree, I was at Indian Institute of Technology Bombay and majored in Computer Science and Engineering with a minor degree in Electrical Engineering.


    Presented at ICLR 2022
    28 Apr 2022

    2nd place in Alexa Prize Socialbot Competition (2021)
    15 Jul 2021

    Our team Chirpy Cardinal stood 2nd place in Alexa Prize 2021. Check out our technical paper and get it touch if you want to know more!

    2nd place in Alexa Prize Socialbot Competition (2020)
    04 Jul 2020

    Our team Chirpy Cardinal stood 2nd place in Alexa Prize 2020. Check out our technical paper and get it touch if you want to know more!

Selected Publications

Hindsight: Posterior-guided training of retrievers for improved open-ended generation
Ashwin Paranjape, Ashwin Paranjape, Omar Khattab, Christopher Potts, Matei Zaharia, Christopher D. Manning
ICLR 2022



Many text generation systems benefit from retrieving passages from a textual knowledge corpus (e.g., Wikipedia) and using them to generate the output. For open-ended generation tasks, like generating informative utterances in conversations, many varied passages z are relevant to the context x but few are relevant to the observed next utterance y (label). For such tasks, existing methods (that jointly train the retriever and generator) underperform: during training the top-k context-relevant retrieved passages might not contain the label-relevant passage and the generator may hence not learn a preference to ground its generated output in them. We propose using an additional guide-retriever that also conditions on the observed label y and “in hindsight” retrieves label-relevant passages during training. We maximize the evidence lower bound (ELBo) to jointly train the guide-retriever Q(z|x,y) with the standard retriever P_η(z|x) and the generator P_θ (y|x,z) and find that ELBo has better inductive biases than prior work. For informative conversations from the Wizard of Wikipedia dataset, with our posterior-guided training, the retriever finds passages with higher relevance in the top-10 (23% relative improvement), the generator’s responses are more grounded in the retrieved passage (19% relative improvement) and the end-to-end system produces better overall output (6.4% relative improvement).

Human-like informative conversations via conditional mutual information
Ashwin Paranjape and Christopher D. Manning
North American Chapter of the Association for Computational Linguistics (NAACL), 2021



The goal of this work is to build a dialogue agent that can weave new factual content into conversations as naturally as humans. We draw insights from linguistic principles of conversational analysis and annotate human-human conversations from the Switchboard Dialog Act Corpus, examinining how humans apply strategies for acknowledgement, transition, detail selection and presentation. However, when current chatbots (explicitly provided with new factual content) introduce facts in a conversation, their generated responses do not acknowledge the prior turns. This is because, while current methods are trained with two contexts, new factual content and conversational history, we show that their generated responses are not simultaneously specific to both the contexts and in particular, lack specificity w.r.t conversational history. We propose using pointwise conditional mutual information (pcmi) to measure specificity w.r.t. conversational history. We show that responses that have a higher pcmi_h are judged by human evaluators to be better at acknowledgement 74% of the time. To show its utility in improving overall quality, we compare baseline responses that maximize pointwise mutual information (Max. PMI) with our alternative responses (Fused-PCMI) that trade off pmi for pcmi_h and find that human evaluators prefer Fused-PCMI 60% of the time.

Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversations
Ashwin Paranjape*, Abigail See*, Kathleen Kenealy, Haojun Li, Amelia Hardy, Peng Qi, Kaushik Ram Sadagopan, Nguyet Minh Phu, Dilara Soylu, Christopher D. Manning
Alexa Prize Proceedings 2020
[paper] [supplementary]



We present Chirpy Cardinal, an open-domain dialogue agent, as a research plat- form for the 2019 Alexa Prize competition. Building an open-domain socialbot that talks to real people is challenging – such a system must meet multiple user expectations such as broad world knowledge, conversational style, and emotional connection. Our socialbot engages users on their terms – prioritizing their interests, feelings and autonomy. As a result, our socialbot provides a responsive, person- alized user experience, capable of talking knowledgeably about a wide variety of topics, as well as chatting empathetically about ordinary life. Neural generation plays a key role in achieving these goals, providing the backbone for our con- versational and emotional tone. At the end of the competition, Chirpy Cardinal progressed to the finals with an average rating of 3.6/5.0, a median conversation duration of 2 minutes 16 seconds, and a 90th percentile duration of over 12 minutes.

Motifs in Temporal Networks.
Ashwin Paranjape*, Austin Benson*, Jure Leskovec
Tenth ACM International Conference on Web Search and Data Mining (WSDM), 2017.
[paper] [poster] [code] [data]



Networks are a fundamental tool for modeling complex systems in a variety of domains including social and communication networks as well as biology and neuroscience. Small subgraph patterns in networks, called network motifs, are crucial to understanding the structure and function of these systems. However, the role of network motifs in temporal networks, which contain many timestamped links between the nodes, is not yet well understood. Here we develop a notion of a temporal network motif as an elementary unit of temporal networks and provide a general methodology for counting such motifs. We define temporal network motifs as induced subgraphs on sequences of temporal edges, design fast algorithms for counting temporal motifs, and prove their runtime complexity. Our fast algorithms achieve up to 56.5x speedup compared to a baseline method. Furthermore, we use our algorithms to count temporal motifs in a variety of networks. Results show that networks from different domains have significantly different motif counts, whereas networks from the same domain tend to have similar motif counts. We also find that different motifs occur at different time scales, which provides further insights into structure and function of temporal networks.

* implies equal contribution