Lilt Raised USD 25m Just Before Pandemic Hit

Lilt在大流行爆发前筹集了2500万美元

2020-05-28 15:00 slator

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On May 12, 2020, Lilt announced that it had raised USD 25m in its latest funding round. This brings the total funds raised to USD 37.5m — making Lilt one of the most well-funded language industry startups of the past 10 years after Smartling and Unbabel. “We closed the round several months ago, but we announced it on Tuesday, May 12,” CEO Spence Green told Slator. He said that while Covid-19 had no impact on the financing, they did postpone the announcement as a result of the pandemic. Securing the funding before Covid-19 disrupted the global economy was fortunate, as the funding environment has since fundamentally changed. The Series B funding round was led by Intel Capital and joined by existing investors Sequoia Capital, Redpoint Ventures, In-Q-Tel, Zetta Venture Partners, and XSeed Capital, according to a press statement; which also stated that Intel Capital’s VP and Senior Managing Director Mark Rostick joined Lilt’s board of directors. At SlatorCon San Francisco 2019, Redpoint Ventures Partner and Managing Director, Tomasz Tunguz, discussed their rationale for investing in Lilt based on their thesis of the AI Agency (accounting firms, law firms, debt collection, language service providers, etc.) replacing the traditional agency business model. Co-founders John DeNero and Spence Green started Lilt in 2015 as a translation productivity tool targeted at individual translators; that is, using adaptive machine translation, which allows the text to change dynamically, suggesting next words or phrases as the translator types. Lilt then expanded their service offering to cater to language service providers (LSPs) and, in 2018, pivoted away from a subscription-based model to take on enterprise clients. As Lilt became a full-on tech-enabled LSP, the San Francisco-based company secured USD 9.5m in funding. The fresh funds did not all go into hiring dozens of Computer Scientists and Marketers though. The company also understood that to expand within the enterprise, they needed people experienced in the complex sales, onboarding, and account management required for large enterprise accounts. So, in January 2019, Lilt announced the addition of former Lionbridge Chief Sales Officer, Paula Shannon, as an advisor. Within months, Shannon brought on fellow Lionbridge veteran Roberto Sastre to be Head of Revenue EMEA. As the rapid pace of research in the broader machine translation (MT) space continues unabated, with dozens of new papers published on a weekly basis, it is sometimes hard to parse through what is of purely academic interest and what has real-life practical applications. So we asked which areas Computer-Scientist-turned-CEO Green regarded as the most exciting in current MT research. He singled out three. 1. Exceptional improvement in personalized MT models. “MT models that are adapted to the particular way in which a translator or organization uses language continue to improve dramatically. Now, by changing only a small fraction of the model’s parameters, we can be very effective in personalizing a very large and high-performance, general neural machine translation model. What this means is that a service like Lilt can use larger models with better out-of-the-box performance — and still train personalized models for every customer, every translator, and even every individual document.” 2. Ability to add more contextual information in translation suggestions. “We’re discovering how to include even more contextual information when making translation suggestions. Document context, project-specific terminology, and translation memory matches will all be part of the input to machine translation in a way that’s more flexible and tightly integrated than past solutions. The result is that terminology will be inserted in the right place with the right inflection, and translation suggestions will be coherent across the different segments of a document.” 3. More efficient automatic tag placement. “Our recently published work (accepted at ACL 2020) on end-to-end neural word alignment shows that MT models can be much more effective at automatic tag placement than was previously known — which will let linguists focus more of their time on translation and less on the drudgery of placing tags. We expect further advancements in this direction in the coming years.” On how their adaptive neural machine translation (NMT) interface has progressed since they discontinued the Lilt PRO software license and closed the platform, Green said, “Our most recent updates have improved overall translation quality, greatly improved robustness to things like capitalization and non-translatable elements, and increased the speed that a model adapts to a linguist’s style and language use.” Moreover, according to Green, “Because our platform is cloud-based, we’ve also spent a lot of time working to reduce latency to make sure that translation suggestions keep up with typing speed.” The Lilt CEO also shared the areas where they are likely to deploy funds in the near-term. “We are focused on investing these funds in four areas: customer enablement, research and product, our service model, and our people.” Elaborating on plans for Lilt’s service model, Green said, “To increase supply-chain flexibility we’re investing in new offices for broader timezone coverage.” An expansion in physical office presence makes sense for Lilt. While much of the language industry’s operational model is well suited for remote work, selling to and supporting major enterprise accounts still requires a certain in-country presence.
2020年5月12日,Lilt宣布在最新一轮融资中筹集了2500万美元。 这使Lilt的筹资总额达到3750万美元,成为过去10年中继Smartling和Unbabel之后资金最充足的语言行业初创公司之一。 “我们几个月前就完成了这轮谈判,但我们在5月12号星期二宣布了这一消息,”CEO斯宾塞·格林告诉Slator。 他说,虽然Covid-19对融资没有影响,但由于大流行,他们确实推迟了宣布。 在Covid-19破坏全球经济之前获得融资是幸运的,因为融资环境从此发生了根本变化。 根据一份新闻声明,B轮融资由英特尔投资牵头,现有投资者红杉资本,红点风险投资,In-Q-Tel,Zetta Venture Partners和XSeed Capital也参与了融资; 其中还指出,英特尔投资的副总裁兼高级董事总经理马克·罗斯蒂克加入了LILT的董事会。 在2019旧金山SlatorCon大会上,Redpoint Ventures合伙人兼董事总经理Tomasz Tunguz讨论了他们投资Lilt的理由,他认为人工智能代理(会计师事务所,律师事务所,债务催收,语言服务提供商等)将取代传统的代理业务模式。 联合创始人John DeNero和Spence Green于2015年创立了Lilt,这是一个针对个人翻译的翻译生产力工具; 即:使用自适应机器翻译。允许文本动态变化,根据译者的类型提示下一个单词或短语。 随后,Lilt扩大了服务范围,以迎合语言服务提供商的需求,并在2018年摆脱了基于订阅的模式,转而面向企业客户。 随着Lilt成为一家完全依靠技术支持的LSP,这家总部位于旧金山的公司获得了950万美元的资金。 不过,这些新的资金并没有全部用于雇佣几十名计算机科学家和营销人员。 该公司还明白,为了在企业内部进行扩张,他们需要一些在大型企业客户所需的复杂销售,入职和客户管理方面有经验的人员。 于是,2019年1月,Lilt宣布增加前Lionbridge首席销售官宝拉·香农为顾问。 几个月后,香农请来了同为Lionbridge资深员工的罗伯托•萨斯特雷,由他出任EMEA收入主管。 更广泛的机器翻译(MT)领域的快速研究步伐有增无减,每周都有数十篇新论文发表,有时很难解析出纯学术兴趣的内容和具有现实生活实际应用的内容。 所以我们询问了计算机科学家出身的CEO格林,当前MT研究中哪些领域最令人兴奋?他挑出了三个。 1.个性化MT模型的定向改进。 “根据翻译人员或翻译机构使用语言的特定方式进行调整的机器翻译模型不断得到显著的改进。 现在,只要改变模型参数的一小部分,我们就可以非常有效地个性化一个非常大的,高性能的,通用的神经机器翻译模型。 这意味着,像Lilt这样的服务可以使用更大的模型,具有更好的开箱即用性能--并且仍然为每个客户,每个翻译人员,甚至每个单独的文档训练个性化的模型。“ 2.在翻译建议中增加更多语境信息的能力。 “我们正在探索如何在提出翻译建议时包含更多的上下文信息。 文档上下文,特定于项目的术语和翻译记忆匹配都将成为机器翻译输入的一部分,其方式比过去的解决方案更加灵活和紧密集成。 其结果是,术语将被插入正确的位置,并具有正确的词形变化,翻译建议将在文件的不同部分之间连贯一致。“ 3.更高效的自动标签放置。 “我们最近发表的关于端到端神经词对齐的工作(在ACL 2020上被接受)表明,MT模型在自动标记放置方面比以前所知的要有效得多--这将让语言学家把更多的时间集中在翻译上,而不是放在放置标记的苦差事上。 我们预期未来数年在这方面会有进一步的进展。“ 在谈到自他们停止Lilt PRO软件许可和关闭平台以来,他们的自适应神经机器翻译(NMT)接口的进展情况时,Green说:“我们最近的更新提高了整体翻译质量,大大提高了对大写和不可翻译元素的鲁棒性,并提高了模型适应语言学家风格和语言使用的速度。” 此外,据Green说,“因为我们的平台是基于云的,我们也花了很多时间来减少延迟,以确保翻译建议跟上打字速度。” 这位Lilt首席执行官还分享了他们在短期内可能部署资金的领域。 我们把这些资金集中投资在四个领域:客户支持,研究和产品,我们的服务模式和我们的人员。“ 在阐述LILT服务模式的计划时,Green说:“为了增加供应链的灵活性,我们正在投资新的办事处,覆盖更广的时区。” 对于Lilt来说,扩展实体办公室的存在是有意义的。 虽然大多数语言行业的运作模式非常适合远程工作,但向主要企业客户销售和支持仍然需要一定的国内存在。

以上中文文本为机器翻译,存在不同程度偏差和错误,请理解并参考英文原文阅读。

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